{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "def load_housing_data(housing_path = './'):\n",
    "    csv_path = os.path.join(housing_path, 'housing.csv')\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      "longitude             20640 non-null float64\n",
      "latitude              20640 non-null float64\n",
      "housing_median_age    20640 non-null float64\n",
      "total_rooms           20640 non-null float64\n",
      "total_bedrooms        20433 non-null float64\n",
      "population            20640 non-null float64\n",
      "households            20640 non-null float64\n",
      "median_income         20640 non-null float64\n",
      "median_house_value    20640 non-null float64\n",
      "ocean_proximity       20640 non-null object\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()  # 查看数据集的简单描述， 得到每个属性类型和非空值数据量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  total_bedrooms  20433 有一部分空值\n",
    "housing['ocean_proximity'].value_counts()  # 查看多少种分类  ，5 种分类，获得每种分类下有少区域"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()  # 显示数值属性摘要"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins = 50, figsize = (20, 15))\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 房龄和房价被设定了上限\n",
    "   1. 设置了上限的区域，重新收集标签值\n",
    "   2. 把设置了上限的区域数据移除\n",
    "   \n",
    "### 重尾 头高尾长\n",
    "   * 转换数据，把数据形状变成偏钟形分布（正太分布， 平均值为0，标准差为1）\n",
    "### 收入特征\n",
    "   * 数据提供的上游证实 是年薪 单位万美元， 提前对特征进行缩放是正常的，需要得知数据是如何缩放的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建训练集和测试集\n",
    "   * 训练集用于模型训练 占整个数据集的80% ，测试占20%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def split_train_test(data, test_radio):\n",
    "    np.random.seed(42)\n",
    "    indices = np.random.permutation(len(data))  # 对原来的数组进行重新洗牌，随机打乱原来的元素顺序\n",
    "    test_set_size = int(len(data) * test_radio)\n",
    "    test_indices = indices[:test_set_size]\n",
    "    train_indices = indices[test_set_size:]\n",
    "    return data.iloc[train_indices],  data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, test_set =  split_train_test(housing, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16512"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4128"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对样本设置唯一的标识符，对标识符取hash值， 去hash的最后一个字节， 值小于等于51 ，256*20%，放入测试集\n",
    "# hash值相同，对象不一定相同，hash不同，对象一定不同 \n",
    "\n",
    "import hashlib\n",
    "\n",
    "def test_set_check(identifier, test_radio, hash = hashlib.md5):\n",
    "    return hash(np.int64(identifier)).digest()[-1] < 256 * test_radio  # 返回摘要，作为二进制数据字符串\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_train_test_by_id(data, test_ratio, id_column):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_:test_set_check(id_, test_ratio))\n",
    "    return data.loc[~in_test_set], data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id = housing.reset_index()  # 使用行索引作为标识符 ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>126.0</td>\n",
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       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
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       "      <td>21.0</td>\n",
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       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "      <th>2</th>\n",
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       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "4        NEAR BAY  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_with_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, 'index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>452600.0</td>\n",
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       "      <td>1467.0</td>\n",
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       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>1094.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "6      6    -122.25     37.84                52.0       2535.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "6           489.0      1094.0       514.0         3.6591            299200.0   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "6        NEAR BAY  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 基于行索引，不能删除和中间插入数据，只能末尾插入，否则行索引会变\n",
    "# 寻找稳定特征来创建唯一标识符，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id['id'] = housing['longitude'] * 1000 + housing['latitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>37.85</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity         id  \n",
       "0        NEAR BAY -122192.12  \n",
       "1        NEAR BAY -122182.14  \n",
       "2        NEAR BAY -122202.15  \n",
       "3        NEAR BAY -122212.15  \n",
       "4        NEAR BAY -122212.15  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_with_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, 'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122192.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122182.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity         id  \n",
       "0        NEAR BAY -122192.12  \n",
       "1        NEAR BAY -122182.14  \n",
       "2        NEAR BAY -122202.15  \n",
       "3        NEAR BAY -122212.15  \n",
       "4        NEAR BAY -122212.15  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set, test_set = train_test_split(housing, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14196</th>\n",
       "      <td>-117.03</td>\n",
       "      <td>32.71</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3126.0</td>\n",
       "      <td>627.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>3.2596</td>\n",
       "      <td>103000.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8267</th>\n",
       "      <td>-118.16</td>\n",
       "      <td>33.77</td>\n",
       "      <td>49.0</td>\n",
       "      <td>3382.0</td>\n",
       "      <td>787.0</td>\n",
       "      <td>1314.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>3.8125</td>\n",
       "      <td>382100.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17445</th>\n",
       "      <td>-120.48</td>\n",
       "      <td>34.66</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>915.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>4.1563</td>\n",
       "      <td>172600.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14265</th>\n",
       "      <td>-117.11</td>\n",
       "      <td>32.69</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1421.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>1418.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>1.9425</td>\n",
       "      <td>93400.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2271</th>\n",
       "      <td>-119.80</td>\n",
       "      <td>36.78</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2382.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>874.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>3.5542</td>\n",
       "      <td>96500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "14196    -117.03     32.71                33.0       3126.0           627.0   \n",
       "8267     -118.16     33.77                49.0       3382.0           787.0   \n",
       "17445    -120.48     34.66                 4.0       1897.0           331.0   \n",
       "14265    -117.11     32.69                36.0       1421.0           367.0   \n",
       "2271     -119.80     36.78                43.0       2382.0           431.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "14196      2300.0       623.0         3.2596            103000.0   \n",
       "8267       1314.0       756.0         3.8125            382100.0   \n",
       "17445       915.0       336.0         4.1563            172600.0   \n",
       "14265      1418.0       355.0         1.9425             93400.0   \n",
       "2271        874.0       380.0         3.5542             96500.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "14196      NEAR OCEAN  \n",
       "8267       NEAR OCEAN  \n",
       "17445      NEAR OCEAN  \n",
       "14265      NEAR OCEAN  \n",
       "2271           INLAND  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 从数据可视化中探索数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分层采样\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 抽样偏差：调查公司给1000个人来调研几个问题，不会在电话簿中随机查找1000人，他们视图确保1000人代表全体人口，美国人，51.3女性，48.7男性\n",
    "# 你的调查应该维持这一比例，513名女性，487男性，这就是分层采样，人口划分为均匀的子集，每个子集称为一层，然后从每层中抽取正确的实例数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115ae04a8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing['median_income'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 希望测试集能够代表整个数据集中各种不同类型的收入"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 创建一个收入类别属性 \n",
    "   2. 不应该数据分层太多，但每一层应该有足够的数据量\n",
    "   3. 将收入中位数处以1.5，限制收入类别数量，使用ceil取整，得到离散类别，将大于5的列别合并为类别5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['income_cat'] = np.ceil(housing['median_income']/1.5)\n",
    "housing['income_cat'].head(20)\n",
    "\n",
    "housing['income_cat'].where(housing['income_cat'] < 5, 5.0, inplace = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5.0\n",
       "1     5.0\n",
       "2     5.0\n",
       "3     4.0\n",
       "4     3.0\n",
       "5     3.0\n",
       "6     3.0\n",
       "7     3.0\n",
       "8     2.0\n",
       "9     3.0\n",
       "10    3.0\n",
       "11    3.0\n",
       "12    3.0\n",
       "13    2.0\n",
       "14    2.0\n",
       "15    2.0\n",
       "16    2.0\n",
       "17    2.0\n",
       "18    2.0\n",
       "19    2.0\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['income_cat'] = pd.cut(housing['median_income'], bins=[0., 1.5, 3.0, 4.5, 6., np.inf], labels = [1,2, 3, 4, 5]) # 把连续值转换成类别标签  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1     5\n",
       "2     5\n",
       "3     4\n",
       "4     3\n",
       "5     3\n",
       "6     3\n",
       "7     3\n",
       "8     2\n",
       "9     3\n",
       "10    3\n",
       "11    3\n",
       "12    3\n",
       "13    2\n",
       "14    2\n",
       "15    2\n",
       "16    2\n",
       "17    2\n",
       "18    2\n",
       "19    2\n",
       "Name: income_cat, dtype: category\n",
       "Categories (5, int64): [1 < 2 < 3 < 4 < 5]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    7236\n",
       "2    6581\n",
       "4    3639\n",
       "5    2362\n",
       "1     822\n",
       "Name: income_cat, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115cca780>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 收入类别直方图\n",
    "housing['income_cat'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据收入类别进行分层采样\n",
    "from sklearn.model_selection import StratifiedShuffleSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "split = StratifiedShuffleSplit(n_splits=1, test_size = 0.2, random_state = 42 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "for  train_index, test_index in split.split(housing, housing['income_cat']):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 11)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>286600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>340600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>196900.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>46300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>254500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19480</th>\n",
       "      <td>-120.97</td>\n",
       "      <td>37.66</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>3.5395</td>\n",
       "      <td>127900.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8879</th>\n",
       "      <td>-118.50</td>\n",
       "      <td>34.04</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2233.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>769.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>8.3839</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13685</th>\n",
       "      <td>-117.24</td>\n",
       "      <td>34.15</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2041.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>140200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>-118.26</td>\n",
       "      <td>33.99</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>1916.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>1.8242</td>\n",
       "      <td>95000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>34.02</td>\n",
       "      <td>29.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>2690.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16365</th>\n",
       "      <td>-121.31</td>\n",
       "      <td>38.02</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4157.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>2734.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>2.7981</td>\n",
       "      <td>92100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19684</th>\n",
       "      <td>-121.62</td>\n",
       "      <td>39.14</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2183.0</td>\n",
       "      <td>559.0</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>1.6902</td>\n",
       "      <td>61500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19234</th>\n",
       "      <td>-122.69</td>\n",
       "      <td>38.51</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3364.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>6.6854</td>\n",
       "      <td>313000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13956</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>34.17</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>2.7120</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2390</th>\n",
       "      <td>-119.46</td>\n",
       "      <td>36.91</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>3.9141</td>\n",
       "      <td>123900.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11176</th>\n",
       "      <td>-117.96</td>\n",
       "      <td>33.83</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>3.3831</td>\n",
       "      <td>197400.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15614</th>\n",
       "      <td>-122.41</td>\n",
       "      <td>37.81</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>3.6728</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2953</th>\n",
       "      <td>-119.02</td>\n",
       "      <td>35.35</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>776.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>1.9830</td>\n",
       "      <td>63300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13209</th>\n",
       "      <td>-117.72</td>\n",
       "      <td>34.05</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1841.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1243.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>4.0614</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6569</th>\n",
       "      <td>-118.15</td>\n",
       "      <td>34.20</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>857.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>184200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "19480    -120.97     37.66                24.0       2930.0           588.0   \n",
       "8879     -118.50     34.04                52.0       2233.0           317.0   \n",
       "13685    -117.24     34.15                26.0       2041.0           293.0   \n",
       "4937     -118.26     33.99                47.0       1865.0           465.0   \n",
       "4861     -118.28     34.02                29.0        515.0           229.0   \n",
       "16365    -121.31     38.02                24.0       4157.0           951.0   \n",
       "19684    -121.62     39.14                41.0       2183.0           559.0   \n",
       "19234    -122.69     38.51                18.0       3364.0           501.0   \n",
       "13956    -117.06     34.17                21.0       2520.0           582.0   \n",
       "2390     -119.46     36.91                12.0       2980.0           495.0   \n",
       "11176    -117.96     33.83                30.0       2838.0           649.0   \n",
       "15614    -122.41     37.81                25.0       1178.0           545.0   \n",
       "2953     -119.02     35.35                42.0       1239.0           251.0   \n",
       "13209    -117.72     34.05                 8.0       1841.0           409.0   \n",
       "6569     -118.15     34.20                46.0       1505.0           261.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "17606       710.0       339.0         2.7042            286600.0   \n",
       "18632       306.0       113.0         6.4214            340600.0   \n",
       "14650       936.0       462.0         2.8621            196900.0   \n",
       "3230       1460.0       353.0         1.8839             46300.0   \n",
       "3555       4459.0      1463.0         3.0347            254500.0   \n",
       "19480      1448.0       570.0         3.5395            127900.0   \n",
       "8879        769.0       277.0         8.3839            500001.0   \n",
       "13685       936.0       375.0         6.0000            140200.0   \n",
       "4937       1916.0       438.0         1.8242             95000.0   \n",
       "4861       2690.0       217.0         0.4999            500001.0   \n",
       "16365      2734.0       879.0         2.7981             92100.0   \n",
       "19684      1202.0       506.0         1.6902             61500.0   \n",
       "19234      1442.0       506.0         6.6854            313000.0   \n",
       "13956       416.0       151.0         2.7120             89000.0   \n",
       "2390       1184.0       429.0         3.9141            123900.0   \n",
       "11176      1758.0       593.0         3.3831            197400.0   \n",
       "15614       592.0       441.0         3.6728            500001.0   \n",
       "2953        776.0       272.0         1.9830             63300.0   \n",
       "13209      1243.0       394.0         4.0614            107000.0   \n",
       "6569        857.0       269.0         4.5000            184200.0   \n",
       "\n",
       "      ocean_proximity income_cat  \n",
       "17606       <1H OCEAN          2  \n",
       "18632       <1H OCEAN          5  \n",
       "14650      NEAR OCEAN          2  \n",
       "3230           INLAND          2  \n",
       "3555        <1H OCEAN          3  \n",
       "19480          INLAND          3  \n",
       "8879        <1H OCEAN          5  \n",
       "13685          INLAND          4  \n",
       "4937        <1H OCEAN          2  \n",
       "4861        <1H OCEAN          1  \n",
       "16365          INLAND          2  \n",
       "19684          INLAND          2  \n",
       "19234       <1H OCEAN          5  \n",
       "13956          INLAND          2  \n",
       "2390           INLAND          3  \n",
       "11176       <1H OCEAN          3  \n",
       "15614        NEAR BAY          3  \n",
       "2953           INLAND          2  \n",
       "13209          INLAND          3  \n",
       "6569           INLAND          3  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350533\n",
       "2    0.318798\n",
       "4    0.176357\n",
       "5    0.114583\n",
       "1    0.039729\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看所有住房数据根据收入类别比例分布，收入占类别百分比\n",
    "strat_test_set['income_cat'].value_counts()/len(strat_test_set)  # 分层抽样测试集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350581\n",
       "2    0.318847\n",
       "4    0.176308\n",
       "5    0.114438\n",
       "1    0.039826\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].value_counts()/len(housing)  # 完整数据集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def income_cat_proportions(data):\n",
    "    return data[\"income_cat\"].value_counts() / len(data)\n",
    "\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "\n",
    "compare_props = pd.DataFrame({\n",
    "    \"全部数据\": income_cat_proportions(housing),\n",
    "    \"分层抽样\": income_cat_proportions(strat_test_set),\n",
    "    \"随机抽样\": income_cat_proportions(test_set),\n",
    "}).sort_index()\n",
    "compare_props[\"随机. %error\"] = 100 * compare_props[\"随机抽样\"] / compare_props[\"全部数据\"] - 100\n",
    "compare_props[\"分层. %error\"] = 100 * compare_props[\"分层抽样\"] / compare_props[\"全部数据\"] - 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>全部数据</th>\n",
       "      <th>分层抽样</th>\n",
       "      <th>随机抽样</th>\n",
       "      <th>随机. %error</th>\n",
       "      <th>分层. %error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.039826</td>\n",
       "      <td>0.039729</td>\n",
       "      <td>0.040213</td>\n",
       "      <td>0.973236</td>\n",
       "      <td>-0.243309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.318847</td>\n",
       "      <td>0.318798</td>\n",
       "      <td>0.324370</td>\n",
       "      <td>1.732260</td>\n",
       "      <td>-0.015195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.350581</td>\n",
       "      <td>0.350533</td>\n",
       "      <td>0.358527</td>\n",
       "      <td>2.266446</td>\n",
       "      <td>-0.013820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.176308</td>\n",
       "      <td>0.176357</td>\n",
       "      <td>0.167393</td>\n",
       "      <td>-5.056334</td>\n",
       "      <td>0.027480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.114438</td>\n",
       "      <td>0.114583</td>\n",
       "      <td>0.109496</td>\n",
       "      <td>-4.318374</td>\n",
       "      <td>0.127011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       全部数据      分层抽样      随机抽样  随机. %error  分层. %error\n",
       "1  0.039826  0.039729  0.040213    0.973236   -0.243309\n",
       "2  0.318847  0.318798  0.324370    1.732260   -0.015195\n",
       "3  0.350581  0.350533  0.358527    2.266446   -0.013820\n",
       "4  0.176308  0.176357  0.167393   -5.056334    0.027480\n",
       "5  0.114438  0.114583  0.109496   -4.318374    0.127011"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_props"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把测试集已经处理完成，用分层抽样的训练集数据来进行分析\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将地理数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 建立一个各区域的分布图，以便于数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a177ee6a0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter', x='longitude', y = 'latitude')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115a84978>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 将alpha 设置为 0.1 ， 可以看出高密度数据点的位置\n",
    "housing.plot(kind='scatter', x='longitude', y = 'latitude', alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a18031fd0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eeSrIys8jjBsXJTM9gt+vLE3BoCQ5+eQnlpFh8OCDCRw6FMXhEEyc2Le4sPx8J9/5Tg7r1vkQAubN85CT0/2LvHx5EykpJl6vjdbWKMuWNfVLYAWDFu+8c4zcXAemKVizxsfcuUnk5p6ZRaw7Fi3y8P77LZx1VmKfxRXA8uUB2tslq1cHmD/fQU7OiLtkNUNMQUEBlZWV9CVdThaoJEAW+OtgmNZ4Hha4XK5hkXjU9HhI0S7CuDLivq1NEy48G1atg1AAPt9nYUiBsEDGNisqOXJQUDcRbr54cOY1e7aLDRuClJeHSU83KClR5rCEBEFCH7VEc7PFzp3K7zZlig2v94QAqqyGqhoozIfyIxa33O3nwBY/Eti9y8att9hobw9x+HCEjAyTq68+3bqUmmqQmtp/RVlQ4KSgwNlzww4ct6Ift2L1h+N/vk9Y5CUDnVf1/POTWLTIc5JbtC9Mn25n6dIwBQU2UlP1/0vN4GO32ykuLh7qaWgGGMvno1W7COPKiBNYAFdeAmOLoeIwvPgXMA2ItEIoAaQD3F5wuOCsiTCmiz8Wvlh+Ok+cFr/l5dn4wQ9SOHbMIivLxO3un4BpaZE8/niQlhYVGPXZZxEeeMCFx6N+4JM8gICGJti1O8rRIxEMQ+ByC9p8YTZscJGTY3LnncksXmwOuADpDZdemsozz9TS3BxFCLjzzrR+9eNwGFx5ZRpvvdWIZUkWLvSSnT3wqxf7K64A5s93MWWKA5dLYLMNgzdDo9H8w6L/wsWXESmwDAOmTIRJ42HmLMH6tRJnUBIJCFypMLdYMGcMXH5O5+dvOwYvl6n7NxXB1DgtgPN6TbzeM/uIHzgQpblZUlSk+ikrszhwIMqMGeqtzkqHO6+BddsgMCbKLo+kvFEQCIJEUF0dJTXV5O9/N8jLg4kTux+vstJi+/YIOTmCGTMGJmh87Fg3Dz2UR0NDhLQ0GxkZ/RdF8+YlMXlyApGIJCUl/h9/KSWHD4epqgqRkGAwdqyLhIQz8x97PANek12j0YxwTI8Hr3YRxpURKbCOYxjw+98a/M9/sqg6IjEMyayZBmPHCpYsga4qHXxSDSkOkMCntfETWPHAME5e1Selcot2ZEKJ2vZNNinfI2gXNhqbJUkScnPhggsSOXZM0NhDgubGRos//jGAEOD3QySi8mINBOnp9rjlyvJ4BuZ/Wihk8eKLx9i9O4BhCCxL4nQa3HFHGiUlfXOJajQazWBi+Xz4tIswroxogQVQXAx//INBczOkpNCreKdiD3xSC0hYnBOfeQQCkmAQkpPPzAI0YYLJqFEGZWUWIBk92mTcuM4Fxbhxds65LIGjSOw2yMkxybUZtLQIPB6YMKH7sRoaJJEIFBYa1NRYVFZalI7g3L2ffeZj9+4ARUUnxJTPF+XZZxv58Y+ze0ziqtFoNEOJdhHGlxEvsAASE9XWWy7Jh9EeFWo9MQ6pjWpqJE8+GaG9XXLxxSbnn9//j7nLJbj3XmdMYEFxsYHD0bVo81t2Lr0QEt1QUQ1XngvZXkhPB4+n+7Fycw2SkwXl5WqsqVNH7uUppWTVqjby8k62snk8Jg0NEQ4cCDJlinuIZqfRaDTdY3o8eLSLMK5ogdUPqqugrVKJEFsc3IO7d1v4/ZCbK/jkE+uMBBaA0ymYMKF3fUwqguVfgNup3ItF+ZDVSQy5lLBhE2zarMToRYshK0tw//0uDh+2SEsTfcqH9Y+GlBAIWNjtp19SUqo8YxqNRjNcsXw+/NpFGFe0wOoj+/bBn/+sxEg4DFddBQsXnlmfo0YJpJRUVgrmzBnclWKL54LXo1YVTh0Lhw+FefdNi4kTDebPPxG0vmETvPIaZGRAdQ0cKoPvPwBJSYLJk/9xLVe7d7fw2mtVZGU5uOWW0Xg8nV8yhiEYO9bJ0aMRMjJOtLEsJaxycnStRY1GM7z5x/0mHxpGrsmhn6xdC0lJMHo05OfDypVn3mdJicH3vmfnW98yufbaM/+I79kLH66ALVt7LmNjmjBvKlx2DshQlFdfDVFTE+WNN8IcOHDi5E2blbhK9kJ2FrT74UjVGU912PP661W43SYHD7azeXNTt20vvthLe7tFXV0Ey5L4/RZlZUFmznSTm6sFlkajGb4cz+Qej02j0BasPuL1Qnu7cg/6fCowPh7k5Aj6m0CzI5+vhjffAbcLAgE4cBCuu6Z35wYCyp2Vmmpw7JiF33/CrZWYqCxXyV7VxoqCYwASoEejkmXLqsnNdTF79tAvz8zKcrJ/fxuWJUlJ6V4kFRQ4uP/+DD76qJW9e4MkJRlcdVUK8+b1IcBPo9FohgDD48GtY7DiihZYfeT886GiQm0eD1x33VDP6ARSwvIPYVQ+NDRE2LU9zLatgrPnO8nN7Vm8FRcbTJxosm+fRVGRga/N5IMPoKRExVzt22+xZVsYQwgWnu2gqDD+z8Hvj7JmTSOFhQnDQmDdfPMotmxpIjnZzpQpXeTt6EB+voM77kgfhJlpNBpN/JA+HyEdgxVXtMDqI0lJcP/9ynqVkAC2YfgKCgHbtoYxbVB71GLfvii5uT1P1G4X3HWXE78f3nsf/vaGwO2GDz6Em2+ySE6sY8/OIFZUkp2ZimF4aQ+AzTyzQtcd8XhsPPTQWNzu4WFo9nhsLFyYMSRjRyKSaFT2q4ahRqPR9JUzKDqh6YRhKA+GP4bRdRLSoUQIuHAxvP13QBgcqYqQnysYPbr3V40QApsNNqxXOcIMA5qa4N13Q6xcFeFQuYe05Hb+938e41AoiYPVAgRcNBPOm0ZcSutkZOiknPv3B3nuuRYiEclll3lYsKDvxa01Go2mtxhJHhzxchE+q12EoAVWt7S3S8LhM0/+OZgsWggZ6VBW7qDNZ1I6R3xZNqe3mKayzIVC4IrFciWnCPbsT8BhB5/fTUtClO2HLCYWmkSi8Pf1kJUMkwfAbfhVJRhUls7UVCVU+8Lf/ubD4zFwuQTvvutj5swzL7mj0Wg0XSF9PqKfaxdhPNECqwtqay2eeCKE3w9f/7rty1p+XwUmTYRJEwX9fXtNU8WWvfKKeuzxwG23OtmyLcD7yy1yswMkFCRjk4KKI5CfA4lOKK89IbAsS7JhQ5QjRyS5uYLSUvOMih5/1aivhyefhNZWGD8ebrutb+5kux38fonNpsRZXwWaRqPR9AUBmPp7Jq58dVTDIFNVJWltVTFXO3ZYzJjRebuysnb27Gll1qwUsrJOd221t0uiUZUv6qvE9OkqFYXPp1ZMut3w5B+8bNwYwNfm5P95xsWzbxm4HFCYC3NLwdthsdyyZRE+/TRKUhKsWQPV1ZKrrx7aVAWWJVm7NkRtrUVpqYO8vIGL89q0Sa02LSyEPXvgyBF1v7fceKOXF15oobVV8vWve3WZHY1GM7B4PNjOiZOL8M/aRQhaYHVJSYlBXp6gpUWyYEHnL1MoZPGXvxwmErHYvbuNhx4qOel4ebnFX/4SIhyGa66xUVoa/5fbsiSGMTDiLSUFWiT8dbP6d3P5JEFpqZv3P4VDOyGcCHYb7D0C0yfDnLEn5rRqVZTCQoFpCtLTJevWRbn8cht2+9AJzfXrQ7z+uh+PR7B1a4gf/tCL2z0w80lOVi7CxkZlfepLKSaAvDw7P/yhXo2o0WgGiTYfcpV2EcYTLbC6wOsVPPSQEynll9nMT8UwVK25urooycmnW2fWr49imqqvzz6LxlVgBYOSV14Js2uXxbhxBjfdZI+7WGgPwV/WgdMES8LT6+CHi+FYE5gWpEgQYXAlwgWTwRXLiyWEcnGFw8rdGImo26F2czU0WLjdgqwsg8OHLdrbrQFbrThnDrS1QWUlzJunkrRqNBrNcEWgvqc18UMLrB7oSlwB2GwG3/52IUePBiksPL2Qb0mJYP168PkkixbF95O7eXOU7dujFBcb7N1rsXZtlPPOi+/b6QtCOAI5Sepx+TElus49G6ZMgANlFrb2MC5hkZXk4HgOXyEEV11l45VXIgghkVJZ8IY6Bqu01MGWLWEqKqKUljpISxs4xWeasHjxgHWv0Wg08cXjwVgQJxfhE9pFCFpgnTFerx2vt/PYolmzbKSlGYRCkjFj4vtjrqxDAiEEpikHpJhwWoISV4caQAIl6eB1QWoCPPELyaP/bxtfrAwSisLP/qmdP/3JS0GBei1mzbKRk2PQ0CBJTRXk5w99DFFmpskjjyTR3i5JThbdimeNRqMZUbT5YI12EcYTLbAGmMLCgREWM2aYbNgQ5fBhi5QUwdy58X8rbSbcMx+2H1Vuv2m5apVJZWWEPz/pI+qLML7EIDXVoKbGYulSP9/97gmxmZtrkJsb92mdEQ6HwOHQwkqj0WhOY+j/B/9DoQVWN7QHYX+NEhXjc1RA93AhKUnwwAMOmpslXm//RUMgAE5n1wlCExww75TVb06nwOMxyM0V2GwGLS0wa5aBz9dDZWmNRqPRDE88Hjg7Ti7C32sXIWiB1SWBEPxxBVQ3qxp/E3LhznP6lyfk4MEIzc2S4mKTlJT4/UWw2wUZGf23xhwog6dehllT4IYlvT8vM9PkG99I5Ne/bqGgQJXTOXIkytSprn7PpT+Ew/D2B7B1J4wphOsuV+WLzhS/XxIMyri+VxqNRjOsafPBWu0ijCdaYHVBeQPUtEBxpnq8vwZqWyA3pedzV63ysX69n7w8Ozk5bt5+OxRbcWjw4INuvN7h8cPd3Ar+ANQ39v3ctDSTb3zDw9/+1s6xY5Kzz3Zx8cWnB/oPJBu2wpqNUJgPu/bDitVw+YVn1mdbm+S3v22npcXi9ttdTJo0tLm7NBqNZtAYHj9N/zBogdUJoSjYDGW5sqS6lYC9FwsB9+0LsnRpKzk5NjZt8tPYGKWkxElyskF5eZSjR61hI7BmToHUZMjsY7ql9nZJfb0kL8/Gww8nD8zkekFLq0oNYbOBJxEam3p/bltblGXLWmhrs7jkEi/Z2UpItbZaNDVZRCJQWyuZNGmAJq/RaDTDiUQPzI+TixDtIgQtsE7j493w1iGI1oG/FhrqITMTLp4CGUndnxsMwspVEaproKDAICPDRktLhPp6B4GAhWFAevrwEFeg8lIVj+7bOW1tkt/9LkJzM4waJfjWt4auBM70ybByLXz4ATQdg/u+0ftzP/iglQ0b2nG7DV54oZGHH84GICfH5PrrnTQ0SObO1dYrjUYzQmjzwTrtIownWmB14O234fd/g6N1EPbAebOhrQ5uW9K7IsYvvgKbtjg5eEhQXRNixjT4+teTME0HtbUWM2faycgwiEah/DCkJENa6sA/r3hSVydpalJlX8rLLXw+k+QhMmLlZMGCGXBkP0wuhXVfwLw5UFDQ87mhkMRmE7hcBsHgySkuSksdAzRjjUajGaYIjqcy1MQJLbBiNDbC6tUwfxK844PaathXA742eOFjuPMiSE6ArLTOz49EYO8+mDrFRuHoDHbsDHLTTTbmzDm9PuE778Oqtaq+34P3QnoXfQ5HcnMF+fmC8nKLadMMknqw6g00hoC8HMjNhkOH1KrI3nDRRUk0NkZoa7O48cbBVbndVQfQaDSaISHRA3O1izCeaIEVw2ZTqQoSBZSOggMHYdcaKBkPu8rh589AQRosWQALp3d+fnEhHCwD07QxbZqtywLR5YfBm6RiiJqaTxZYfr/kjTeiLF5skps7/H6EnU7BffeZ+HwmXi/d1kH0+eDdd6GmBmbNgoULT04HsXVrgA8/bGPGDCeLFyf2S3SUlsLmLVBRAePH976gcmqqjfvuy+zzeGfK5s0tvP56HQsWJHPppbp+jkajGSa0+2CjdhHGEy2wYni9MH0u/PYZ2L4ZjjVAow2ONoLbCV+fBl43LF93QmAdbYCWNhiTr5Jy3nozrF2vrFlz5yjR1RlXXgpL/w6TxkPhqNOPy/gnZae2Ho5UQ/Eo5Zo8E2w2QUovVlO+8gocPAhpafDWWyrNysyZ6piUkldfbcXrFXzwQTszZrhIT+/641hRYREOQ1GROCnmKy0NHn5I1f1LTh76eoc9sXWrj2Awyrp1LVpgaTSa4cUw//78qqEFVgciJsyeJln1uYRUAUmCaDr4gPePwfaVMG2iEkDVjfD7pRAMw6JpcOUClYPp/F5YWItGw0P3dX7M7RbcdlvkX44nAAAgAElEQVR835ZWHzz+LLQHID0FfnBv1+Ivnhw8CPn5x4tiq8LHxwWWEILCQhv794dITTVJTOz8yq6qivLEkyE+/UyQn2dwxeUGN9108uQdDrV9Fbj44nTsdoOZMz1DPRWNRqM5QYIH5mgXYTzRAqsDozIi/O/PokQT7EpFNQG5AtKgbheIrRBtgM07IMGrxFWiC2r7kB6gvR1eew3KyuCccwanIHBbOwSCkJ2hcl6FwmcusHw+WL8Bxo+DvLzO20yYALt2QUqKal9UdPLx229P5siRCJmZJi7X6QLr0KEIP3ikhQ8/MhACDmTYsKIOrrtOYrcPP/dpb8jNdXLLLTlDPQ2NRqM5mXYfbNIuwniiDYId2L4pwPwpIexRIB/IkGplxRcQXq/yY4V9sPRtGJ0F506HgkxYclbvx1izRomO1FR4/31l1ekrh8rh78vVbW/IzoTFCyAagasvhoQ+5AOtPQZ/XgbPvAf1zSf2r14DL78CL7/a9bnXXw9nn62e6w03wJQpJx93Og1KShwkJXW+dOXjj0NsWG8hDIhEDHzNkubWaFzFVSAAW7fBkSNx61Kj0Wi+egiUIojHpgG0BQtQ8UBSQjQq8TglHofFsToB2QLagKOAE0wTZk9VyUdNA644u+9jRaMq0Pu4BcnqY/m+Y03w1LNqLqvWwiPfhdQe4qGEgIsXqa2vPLtc1WS0JLy8Ah64Wu0fNxZGj4bZs7o+1+2GK67o+5jHMU2DqJSkplo0Nlikptu4/NLOxdWxJvholXquFyzofZzZ60thy1ZwOOGhByBDh0VpNJqRSIIHZmsXYTwZ8QKrvj7Cn//cSEODxf/57wQiYQtsQch2QbpQarwEbBZ4WiEjF264qv8xP2edBfv3w+HDalXdqE6C3LsjGFQiLTMdjtaC33+ywAqH4dO1sK8McrPg4nP6ZrE6FV8AUjwQtaCl/cT+oiJ49Mf977cjXaUtuPZaJy+8EGHP3hAlRSYXf83k6qs6f+GfXwq1DYCEmnq4/47ejd3UpIRgMAjt/jN4EhqNRvNVpt0HW7SLMJ6MeIG1Y0eA6uoov/m9SdSFygWCVAnXWgUiC5xJMDoTbpgP0/Ng+pQeOu2GpCS4/34lkj78CH7+v+Cmr6t4pVMJh6GuDrKzlcUKIDsLLjgX1m2CCxZBbodwHsuC//Mn+GwtTJ0AldVQ1wjf/LoKKdu7V8WAFRWp1Xe94eoF8OqnyjJ08wDEi61aFeW99yxSUuDWW21kZ58QWl6v4I03Elm/3o3DIRkzxiAj43T7s4yJquyME/d7y3VXw0crlNAd1YsEpRqNRvMPyXEXoSZujHiBNXq0g0/WOohigU1CGmo5oQSCMKsAWk2YUwDHWuM3rmnCunXKtbVrd+cC6+VXYcsWOO9cWHK52icEXHS+2k5l7yF452NIS4bte2HUaNi0Ai5fDBu+gJUr1Yq+xEQl8tI7qUG4YwesWgVz5sDs2TBjLEwYra49Zwfj0cGD8PEKmDoF5s/v32tQXy95550oeXmCpib429+i3HffyR9Jt1uwaFH36YWFgK8tgmUr1Nu25ILezyEnR6XX0Gg0mhFNggdmahdhPBnRAisUgmafg3DYDvYoOCy1xC7BCVJAAOaUwOY6ONwAVY1wXi8TWfaGm2+CHTvhnIWdH2/zqZxabe2dHz+VSBSkpRKZCgEtERWYf6hKBdcXFyuBVV4Oe/bAggUnnx8KwUsvKQH2+utK9CUmqoLKhyvhzWWSsoMREr0Wby23kZBgkPWx4LFiyMrq+/OPRAAEpqlcroFA/xOAnTMPJo9X99N6kaNLo9FoNB1o98FW7SKMJyNWYNXXw1N/hj0HIBx0kZLcRlMYMAXYBUQEBfnQ7Idb5yq9ZROwrRyun6MSi54pY8aorStuuVllKC8p6V1/uRmQkqgyp7sc4IzC9ZfBtLHwkUvFayUkKGHj7iQuy2ZTK/5qa9Xt8TizYBCefBZWfxZgwxcBfKlOkpCkJdkAk40HBHMckJF8cqb2nsjOhnnzBF98YdHQIliyxCQaPeEO7StaWGk0Gk0/0bUI486IFVgfr1BL9EuKYOdeO+MmJrKpVlDTbsNmFxRkqlVyo7OU2ynRAaGIWj3YTXUYdtbAO/sgyQE3TIGMxP7PMSnp9NQG3REIwqgcsNsBCeeUwsO3qWM33wwvvKCE5YwZMG3a6ecbBtx7rxJ1BQWqHynhjfdh6QdRdqyMEom4INlGQwb4PRYi3eCJ9wWTdip34g2LTmRTbw3C3kb1mk1KB+cpnzYhBFdfbSM9R/LqO/DpWkF2Nsyf3a+XS6PRaDT9xe2B6dpFGE9GrMCyLGVtSU+FjDSBO8HBXZfC66uhvh3cBfDv34aEJHhuNTT41Eq6a+d0XY6lLQQvbIc0N9S1w2s74b65g/ecsjOhoRmam8EfUuLxOOPGwaOPKjdgYmLXlqakJJg8+cTxQ4fh0zVQtkcSCZqABSkSEgzag1Dvg1Y7CCd8vA1mj4Wx+eALwR82Q4NfCdSiZLh3Btg6ee0SEgQ2U405EGWC4kmrH15fB1leuHRG3yx2Go1GM2zx+2C7dhHGkxErsBafrwo6H6mCc+bD+Gnw70/Akd2SUAjSgS1bBNdfDtdMA9MB2SmQ383qu3BU5Yty2dRtW6j386mvj9LcbFFcbOu2gHJHolF5Ul0+mw2uuVSlaXC5YO4pxaZ7KilTXQfPLgVfO1x7McyYBP6A5JV3Jc3tQpnuLAGJFgQBE9oQfH4IdlZDig2uqVcCq6wZGgNQHHPblTdDtQ8KvKePO2OyWlUZjUJphzlbFix7H7Zsg9kz4WsXDb2gOVgLmw4pF+yC8ZCc0Pc+JJI1BMjHZDRfkRo/Go3mHxvtIow7I1ZgZWXB5dfDG6vAkwZBAUfLJM1HLJCC3fXwy1bYfVjgsENOBtx5NWzYpixYU8fHXHEdSHHDotHwWYWy1Nw2vXdz8fstHnusldZWi1tvTWTmTGen7bZsgW3bVD2/7dsj7NghufVWg0mTTlwVN1wGs6aA1wP5fazI8u4KCIbUKsTX3oNJY+C1dRHqW0118bkN8BuqhFC+AZYgmA3CDnVOCEahLWaBshvKGhUJQ4sP/EGwd3HxmibMnXn6/kNl8OlKyM+DFZ/ChPFQFMdFBv1hTDbMGwMZXlX8uz9EgU0ECeLQAkuj0QwP3B6Yql2E8WTECazqOmWhycmEN9YoS8/aXeA0oakWiCgTiQzBrq2ShCzBVV+D6np48iWojeVYmjtDiZlTuXQ8LBitxITbfvrxzhBCiTXDOF20AUQikrVrQzz3nKCgwMHOnZK2NgvTlOzbJ5g06URbhwMmjT3xeOcu2L4DFpyl4qo6I2SBw1AWsHBEBcGbhnKJvltmQLaEZtSTsgP1qj4jxYBTYnoERjIETGiPfaLGpsIELzz/d2hvhdHJUFkI2bG51h2D1jbISFFisDPMmNswHAZE167ZwcTjglvPObM+bAjuJXnkXXwajWb44vfBTu0ijCcj6jt+zyF4ZqlyPRUVqLQGm/dBzTH1w1k0G7avANyAtDDbJcE2wScbBGdNg6oaGFuoXFm7D3Q9jtfVt3m5XAYPPODF57PIyzv9LXnxxSbWrAmwcSPYbMkEgw78/hCWBRMndv0WBoPwwstKqFRWwSPfP/l4SwSea4DKIIxywqWLwL9cCdBbrwJ/FGocBoZHYmUDrUBAQm6sAx+QAlE72NuVG7U1VvrHNCA3CFOTYNRYpcuWfgJTSmDLHnjzUyWYbCbcfSUUdlIwunA0XHYJbNsOV1wGo/uY9X6gCYXgg09g7SYl1M9fAPPn9M6N6UAHb2k0mmGEdhHGnRElsHbsB7cTstKhvArOXQifbobMFDhrIhzwCoIVsL8qhGz2Y9ogVGviHpPInVcb7N4Pqzeqvs7vpsBzJKJ+fBP6EJ/j9Rp4vaebaEIhi127AhQXG9TVRTh6tJ3Jk03sdoO2NkltrcX48Z1fFTYbZGZCVRVMnnj68WXNUB2GQpcSWesT4L5bThzfVg+WU2AzBKGQBS4gAbBLiEhoVsWwZasg5BG4TRjbweMVCEGqGxJjqxEtC5pa4e2VkJcJdhs0++C1D+GRTkrbCAHnnqO2k18TlZE+uY9pIbqjqgqWvgmFhXDpJb2zlr3zAazdAAX56j1/411wOWFmJys0NRqNZljj8sBk7SKMJyNKYI0vgnXb4FAlFBfAhbNgdwXUNamCxkUF8PIzgjtuD1AWCuNKkNRXG1ybY2d8kYuxo1XslRCqbWccOwZPPaVuFy2CSy45szk7HAZjxzp5+eVGqqsjPPqom2nTTJ5/PorbLRgzpuu/HKYJ37oH6uohL/f04/URSIqd7rVBQ+Tk461hGG+D6hCQZIDfgoCAAxa0SJBRMCWkSOzTbRRNM+ioEWdNgPU74XCNyt86e5KyWEmpxBWAJwGO9qG0zbFj8OSTqobg9Blw4w3xcR1+8ilU10DFYSid03Pi1GAQNm6B0QXqdbbbVH3IVeuUwIpGJRUV0NamRG7HEkAajUYz7Aj4YJd2EcaTESWwpo6DB25RLrCSUcrCc8+lsHKbij1aMFVZszIzwtTVWciojXZ/BF89vPUWLF4MJaO7H2PrViUC8vPh00+VyOqLJaszbrsthfR0g8OHw1xxRTKJiSaPPmpimqqUTHe43V271uYmwqsNqhRQwIILTimdk2HA4c2Qk6csXaGIAUELLJQpOWKDYATqLYJrJUYSpHWIpxqdA/ffAAcqISkBpsaSqmalwZE6SE2CmkaY14dcXzt2KnE1ejRs3aJWg/Yni/ypTJ6sYtUKCiCljwlL23ySY42ScBREuuDtt2H5cguXS8XUWZZkxgzBddcZ2O2CI0dg3z6Vjyw19cznrtFoNGeMdhHGnRElsABGnWLJ8SbC5ae4++bPt7Nxo8QwJA67je3bbSQkKEvF5Zd3339GhhJrlZWQlg7OzhcEdouUkt27I6SmGuTkmDgcBldddfKvvsfTP4uILwANbZDpgbkeSDbhSAgKnDDu1NixEBQmQtAO7hQo80HQRKW1l4by+WGCR2B4JY4g2E5JTZGXqbaO3HUFLFsFNQ1w7my4cF7v55+WqtxxVVXgcqucXvFgxnQYP04tEuhNJnmnE2bPgL8vs3j/7TCNxyK43Rals10sCxoEg1BSIliwQFnsNm+G9HTJhRcK/vxnaG2FQ4fgnnviM3+NRqM5I1wemKRdhPFkxAms3nDttR4+/7wFw7BITnYRiahf3LRucmAdZ/JkuPMOaGyEqVP7V/alocHij39so7jY5HvfS+qyXShkcehQO0VFbpzOngdq8MEfPoH2EKQkwHfOg/FutXVGRjKU5MHG7cqFajrBHRGEBERdQFQqa5Y/jJUkqGyxsXYHFBd1P4/kJLipn67TSZPgttug6qgSRfESWO3tFn6/hd1uQi8C0Jt94EiGNTskR5qi2CMRfM3w+co2srNdZGcLqqpshMMCu12Qmyt58y1JboEkM1PQ2hofy5tGo9HEhYAP9mgXYTzRAqsTZs40uemmFMrKJC6XYMkSQU6OCoDuCSH6Vt6mM9LSDK67zk1OTvfBRZs3N/PkkxXcdls+552X0WO/+2vBF4SiDCivh/IGmJLfzTy88D9uh78ug+WfKlE2LlewdY3k8JEIwagAnwRMXMEgk8Y42F3Wv4AoKVXeK79fuTSTutCVQsD06WqLFxs3tvPGGy1ICcnJBnfdlUZWVteXhq8d/vAm+PyAS2BkGrjDNgI1YUIhSTAIR49apKREME0bINh3ULB+k4X9ObhgEVx1larFqNFoNMMC7SKMO1pgdYLDAd/+tqCmRuDx9D0mp7e0t0tqaiSFheKk7O2GITjnnJ59i8XFCVx4YQYTJnSRSOoU0j0qw3x1s3qc2gvrT1Eu/NM9UFoCr38IBTlQVWXQErbR2AAuWwhpQEqKQboH8vspGt7/QCUTNUzwJsF37lWrBAea+voIr73WTG6uDYfDoL4+wosvNvH973ctWHeWwbFWKM6FixdDVYXAJw3shqS01KS5WZCUJHA6LfwBicspKK+Q5OYJ8nMFB8thydcG/rlpNBpNr3F5YIJ2EcYTLbC6wG7vOjHnqUQiaiVbX1ezvfJKlM2bLW6+2WT+/L7/dcjMdHLTTd2YoE5hbBbcPh8ONcDEHMjrpXBcsw6WL4fdO2DFZzBmDExwG/ibLOqP2kjEwpNqw99okNWP7ObhMHz2ubJcmSaUV8DuvTA/TnUcKyrh8T+rRY+33aBct8c5WBklaBk4HOrNy8iwUV4eIhKR2GyduwpDEbUaEmDCeIMfPOzgi81RkpujzC012bpdsn67QbDd4J0VBjIKdkOSl2vQcAyuXRKf56XRaDRxI+CDfYPrIhRCmMB64IiU8gohRDHwIiqV9UbgDillSAjhBJ4B5gANwE1SyrJYHz8BvokqkvF9KeV7sf2XAr9B2eWelFL+Ira/0zEG4vmNeIFVXQ0NDaoYcnd1+rrio4/UlpgId9zRe1EGKiA+IUH0yUK2ZUsNyckOior6t/xsaoHa+sK6Tcpy5UmCV99TsVljpkF+lsGuvQLDbzJhnKDJD+t2wFV9FBDHM9iHQmrVYzQKjl5mwe8Jy4L/+c9QVgaYUFkOP3wEDrZAbTPsrLCzqS6B5PQQ2V5l0fJ6bTQ2SrKyOhdYJXmq39Z2SHCC3xLcsMTGlEwnL70c5mCdiWUX+AMm+ypg5gTILzD50XcF3iTISO+0WyxLUl0dxuEwyMgY8ZemRqMZTIbGRfgQsAs4XqX2P4BfSSlfFEI8jhJOj8Vuj0kpxwohbo61u0kIMRm4GZgC5AEfCCHGx/r6HXAxUAmsE0K8KaXc2c0YcWdEf4s3NcHjj6uklYsWwZVX9u386mr48EMlqlpb4dVX4eGHTxyvqAjT0mJRVGTH4zndvHX55SYXXQROZ+9XBNpsAtMc3JoxY4rgk1VKVKSlgZkAR1uUIE1KErgTYP0R2B6F7Cz44CBcVNL7/k0TbrwOXnoVInUwcQJMmRyfube2QkODxOEGm12QkAAvr4QyA5rqYWy6QclYJ2VVAYJNURISDFpb3fz61yHuvtveaRLXvAy4+zJ483NVBWD6GLhiASS47GAzaXlBclmRYMs2g8qjkuKxAo8bMjMgqQtvrpSSV189xubNfoSAa65JYe7cOEXwazQaTU84PTBu8FyEQogCYAnwr8AjQggBXADcGmvyNPAzlPi5OnYf4FXgt7H2VwMvSimDwCEhxH7g+Lr0/VLKg7GxXgSuFkLs6maMuDOiBVYkcqLOnd/ffdtt2+DIERVcnZd34nwpT1hgAoET7bdvD/L8860AZGSYPPBAMi7XycJICNHnNA5Tpgz+0rOLF6t8TQ2NUCdgVxk47SqB6G8eUcHw33sFRrdAoReW74FzRoOrD5+uyZPg0R+p1zA5OX51B10uKCmAjdskM2dAJNNie6Kg0W9gZkB6CowpcLB4ejp5CRamafBf/xXC54NAQHbZ77hR8MObT99fNNogPQPcCTBtKhypFgQCcNt1XYsrgIaGKJs3+yksdBAMSt5/v0ULLI1GM3gEfXAgbi7CDCHE+g6Pn5BSPnFKm18DPwaOL2lKB5qklMdTXlcCx2Ng8oHDAFLKiBCiOdY+H1jToc+O5xw+Zf/8HsaIOyNaYGVkKLdeTQ3MmdN1uz174Nlnlfvqiy/gkUfUKre8PJg5U+U4stng5g4/uDt2hPB4DDIyTCoqwtTXRyko6Fw11NZKNmywOP98o8fEoUOB3Q4zp8JTL6oSQy4DivJhVJ6k5kiER38cYfk6G1bYwLSbeHNhkQVX9NFV6HarLZ44nfD9BwV/eRpaUywOn+enNWDnUpeD1qDgO7Phqe3wcoXBAzMN8jxw330OWlokY8f2XeWlp8LVF8FbHymL3/3fgOsuUZ+P7nC5BDaboK3Noq3NIi1tRF+aGo1msBFA/Jwj9VLK0i6HEuIKoFZKuUEIcX6HGZyK7OFYV/s7eybdtR8QRvy3+MSJauuOhgb1A5mXB+Xlyu2UlKSsLDfeqPYfOKjaRSKq7ZgxdjZuDNDWZpGUZJCW1rVz+9AhyYoVkmnT+hbDdSZYsaLMvbUU7TkAlUfh3Gmwbj8cOAyXzIhy331+du0JgBUFTPAmkmAm8qc/wYKze5c7bKAZPx7++WeCyhC86JQcPRLFVg+XFYNNQHMA3A44nkosLc1gb5mkfgPMm9WzODqVeTNh+iT1WfB0YoSqrFS3Hd9rj8fktttSeffdFnJybFx7rU7xrtFoBhGnB8YOmotwIXCVEOJyVJVbL8qilSKEsMUsTAVAVax9JTAKqBRC2IBkoLHD/uN0PKez/fXdjBF3RrzA6g2TJsHKlUpcTZx4cv6igwfhb0vBtMGmzcrV+LWvwZw5TpKSDI4dizJ+vIOEhK6VTGmpqimYkdG59ero0SB/+UsVaWl27rorF5frzCIRo1F44lloa4cH7updKR8Z0/j5aZA+A45UQ1uDZO++drAiqD8GEWgJEUpwUXnE5PE/wcUXwtzZZzTduGC3Q7Hd5HsyEftogbNQvdarDoOIwLdmQ3rMevZfjwd57Q3l773zdhf3f7Pv6fhdTqCT0w4eVLUUpYR771UrMo8zYYKbCRPibMLTaDSa3hD0waHBWUUopfwJ8BOAmAXrR1LK24QQrwA3oFb53QUsjZ3yZuzx6tjxj6SUUgjxJvC8EOKXqCD3ccBa1A/SuNiKwSOoQPhbY+d83MUYcUcLrF6QmqqC130+lROro9Vnz17YslMFfEciELZBwAkTiwQTJvRuWaJpCjK6yRO6Y4eP1tYITU0RqqqClJScWXHDaBSamiEYUkWYe9PbhDGQlw37D6pcVZecByGfwIoed2UbsS1CqL2NtAw3bredl15Tr9/Y4jOactzwiJOF7tw8GJMK2bH4qOrqKKs/D5CcYmBZsOIjP1+/xkZ6+umidtN2OFgBF50Dyd7TDndKe7taLQk9x/1pNBrNoBFfF2F/+R/Ai0KI/wVsAv4U2/8n4K+xIPZGlGBCSrlDCPEysBOIAN+VUkYBhBAPAu+h1kY+JaXc0cMYcUcLrF7icHTu7mrxKUtQYgLUtcAX+yFnL6zZBt/9OuTHISZ92jQPW7b4SE21kZfXj+KGp+BwwAN3K0GY0stkni4X5KQrQel2QWE+pKeaFBTYOXw4hKqZI/AkCabPCHOsIcTGrR4OVrpofxK+eQvMmdrTKLB2FwRCcO6MM3iCfcBunhBXAH6/pKhA0tQuCAYh0S5oa5Okn5JaIRqFV98FXxtkpsG5p9Sz7IpJk+CGG07c12g0mmGBwwPFg59oVEq5AlgRu3+QE6sAO7YJADd2cf6/olYinrr/XeDdTvZ3OsZAoAVWNzS2QyACed1YJ0aNgmnTlPgPOKFVQKoX/PVwrKX3Aqu9XZVgyerEkpWd7eSHP+xFnZ4+0FuLy3EqKmHjVpg6Qc311TfhfzwkWLo0lXvuqae2LoJlCZI8Fsfq6zjWbFBR1crchXmMK3Hyt+UwdVzPxa9dTlUOZ6jIyzMpLDTx746ye48kMdFkwwaT0aNPbmeacN5ZKjZtfB9TUpzVSzGm0Wg0g0bIB+W6FmE8GXCDoBDCFEJsEkK8HXtcLIT4QgixTwjxkhCiH+k9B576NvjvL+C/1sDWo123mzUdZk6HiB1aLbBMePMzSPZAUV7vx3vudfjlH6Cu4cznPhB0DIo3bbH0FsCsWQ6++CKH557NYNE5TkzToqlJkp/nIBwy2bqukd07gkQiqkxPT0wvgbk9LDqwLMmHH1pUVcV/8YfTKbj33kRmz3Yxfryb/8vee0fHcZ5pvr+vqjp3A41GzgBBkCCYRIokRInKtAIlWbQkK1nBQbalWWePw/Xce8/smZ0NHu/O9YQdj9e2ZCs4SZaVsxhEMecEghE5h2507q5w//hAExIBRpCSPfXj6QOyu6q6OhD99Pu83/PeeKOf5uaJFd8NV8FXPwcl9tBmGxubP3eOB41OxcUGuDiO6/Gk1uMcT1GtB0aQKaofOxJZSGbBsCCSnnw7rxe+9Dlouhxuvh7uvxkaquHeG8B/Fq1SM+pkn9PZ7HMxqaqAxpnQ1gGDQ7ByXASDy6XQ0OAilfJiml6yWQfhEQdGVhAOu3j+txad+02Siak5F9OEgwctBgfPXGCl03KV55ng9SqsWOHCFXBxtE1w5ZXneKI2NjY2f04oU3SxAS6wRXiWSa0fKypz4dOzIZqBJaeJTlBVWDALnn0X4kk5ILn0FE3rE3H1Unn5uKJp8JlPS3HldkHOhyxGn0+losJNZaXKtm0W+/ZZ5OaquFw+qqoMfC6Tw4cVlkyB861pgsceUxkchJER2UR/On75S7mC75FHYPr0028/mgGzVI4FsgWWjY3NXzxOP1Tbw56nkgvdg3U2Sa0fQAjxJeBLAFUfboC5CAgBl55FvuuiRmkLRhMy5XuqZul9nFAUKCo8+fruXnhrDcQyKolImoEBL06njqpCeXkWVXWQk6NQUjJ157J9uxxNpCjw0EMy6+p05y7E6fu7LAsicXh5NxwehnAaNu+CJRep6d7GxsbmIyETgw67B2squWAC6xySWj94pYzV/ynAokWLLljS6lRSf/F14JSSyVi0tGTJZi2mTdMIBk9vpus6PPEbGOjN8PILvcQiJpqmkpPjweFwc8klDu66y0FFBbz6aoyhIZMHH/RSVXV+b73du2UVLZOB5ubTC6yHHpIxG6cKPo3G4PHfwHOroT0DiRioPji2H/7h63DNFed1ymdNJmPxzjtZenoslixRmTPHXpNiY2Nzgfhohj3/RXMhf2OfbVKrzUdIJmPx+ONRWlt1hACfT+HRRwMT5j+NR9chHoe3X+9nNJzFshT0VBbDEMyf7yEYdHLkiKC4OEtrq4HXCxs3pk8psI51Squ1ugwCk4zjW7wYfv1raV3OP4Pq0mQxGxcy8GQAACAASURBVOPZvAP2t0A2BYMjkDUgzw1eH7z29gcFVmuHfOx1NRdu1eOqVVnWrNEJheCZZwy++lWF0lK7wcHGxuYC4PRDpW0RTiUXTGCdQ1LrXwwDA/Dkk7Kx+v77oXpqExYuCG1tOm1tBrW10tvs7NTZujXNjTeeuuve7Yali+AnPzYRqPJbkAAhdC67TKO8XHDkKHzqUyoFBQqRiMncuZP7p2u3wqtrZJhprh8euxdyxnKqTBO6R8Dvgtmz4Xvfk/1vZ5JE/2EGh2S6+/i4iuO2rhEGpQecuSAGZIN/aFxeWHsn/PRJMEx4+G5oPE317Fzp7bUIBiEYVAiHTSIRi9LSC3NfNjY2/8HJxqDbtginko/Cc7hoKaoXC8v6YBVj/XqIROTg4tdfhy9/+aM7tzNFjsI54cQqyolohlOh63D0MEyb5uFgOk46LdB1kxkz3JSXO4nH5dzGvDyFb3zDTzbLnwZatyXglT7oSECDH24pkQKrolhWnA50wrO7oLQIjBH456fh4GEpir5xD9x0ubT1ykvOTmRt2AovvSWrX5+/F2rGJlYtvkSOAOrrhtgQuAS4NSjywx23ntjfMGXkhGme/jmKRuG116G3R4rCq68+89mGTU0qTz1lEI2a5OcLqqrs6pWNjc0FxP4VM6VcFIF1Jkmtf64cCsMzh6DQDQ83gM8BBQVyDEoyeeq0btOU1S6vV4qQibexaG7W0XWLxkYHDseF8aOqqzVKSzWOHcuiqgKHAxYuPH1q/Ogo9A3Avffl8cbr0NebpWGGj4LCEGveF+QE4LvflttqmviTuNgxBF/fAYeHIBUDTYd/z4Urs2CMgj8HthsQH4COjbBrMwx0gypgJAqPPwfvrIKqUigIwaMPSkF7JmzdBfl5EI3DvpYTAsvthntXwp23wEsvwb790NgIy6+H3HEVrNoquO02mTh/quqVZcHTz0Bnh4VQDF54QWEkquALwvQaqK859Xk2NGh89asKkYhFRYWC1/sRJrDa2Nj8ZePwQ7ltEU4ldtfsebKmG1wKdMTg2CjMyYelS+UHciYDcyYZD2NZ8PtnYfceaVV97uGJrcTVq9O8/HKSVAquvNLJffdN0pR0nsiATT979mTIZKChwUFBwek7Hjs7Yds2iMU0amsLeeQRk6VLBI8/K9C7weMDz5jFl07Di2/B23vhLQ16iyEzDEYcSMPAMOw6CgsdUF8HpeVQ6YN9/TDUDdkIZDWLlE+wtw2CDqheDO1d0Dd4QiidjkXzxypY6sQCyeGAO+6Ql4kYisEbbbKSVT8Nqj7U2zUYhuEhaD0K+/ZaRCJpBgcN0mnY2+Vi2ZUa63fAdx6BnAmEdSIBb62CgB+uuVL5wHBxGxsbmwtCNgY9tkU4ldgC6zyZE4IXW2XlqnRM+yjK5MLqOOGwFFdVlTA4CBs3TSyw9u7VaW420XXo6Ehx660eAoELU8f1eBSWLHGf8fa6Dn/4A1yxBNau03nlBYvXXzLxeFSq5ygsvlzB7QJFwJ49WZ57UWd7s6A5z8lIWCHTAMYgkES+EwMWiVHBJgWCLmgsBUuH5t2QHgJSJhhA2GK4UBBNCjq6Ze9U3hnOVATZM1Zfe3IP1pmiKPIxWUL+HM+2ZvjdW7D5HZhVBdu3mUSiJjNnqKTTkEqZ6AY4tMmb43fvg/fWy/6yulqo/jNfnWpjY/NnwMdj2PNfFLbAOk8uK4H6IHhU8J5F9pXXC34/dPdAKsWkzcuhkEIsZhEICPLzFfbv1wkGnRQVnVnA5oXENGVVatcuk00bIJ1WcLkEKd0kvEdQUWvy119WeOWlDM8/n8ZSBIcGLYa7DAy/F2NQyBnRlgmjBnRZsMoi2+hgnVehuATaR2A4jFyHmkG2iTkAzWLZlYLrF8gE/Nwc2Y8FsvJzOgryJ74+HIZg8NT75nnha9eCbkJRADK6TP7PdcPeI+D3gGlAIgP5lYK2zdBy0KK2yuCBexSmzXRSWzn5eZaVyOHhPi+EPuLX2MbG5j8IDj+U2RbhVGILrCkg/8yLPn/C5YIvfA62bpMfoosXT7xdY6PGokVOiooUwmGLgwcVdu+GwkL4znc+2sHITicsWwYvvWSh6+D3WySzkNZUlKxFOiN4ZxW88WIWj0chFRdkTdBHDITfgJgGuWPd4m0GtAvwW7BfJx5wsn+trAyqftmXBYAXKbScgoa5J9Lvd++H370kv4TduxJmzzz7xxOJwO+eh1tvgrLTrNYLjVUrh+Pws00QScKSKrhsDhzphIVXwIx8WLBA4VCHk0hfihl1Cldf7TntwOuqSvj+t2QFy/mxnNRpY2PzF0c2Bn22RTiV2ALrImIYsjHa54F9++DQIaishIULJxdK8+c76O+3OHhQp6nJQU6Oyr59sin+XMXVli1dbNzYxSOPLMDjOfOyWyZjsX+/DPicNk3e+fLlcOWVgu5uk7SuoDhBKKA6wOOFrg4IBgVg4vcLBgUYFZDKh1ETOADETQiqoJowXwULzLRFXo5gVhFs3AMxB6Aq4APS4J8JsXHDtN/bDMFc2dv23qZzE1i5ufDJFROn1U/GoQEIJ6EqDza3wyc+AT/4nLQRj8c+XLZYA86grDaOM23Yt7GxsZkSbItwyrEF1kViOAKPvyh/lvihs1mKpA0bZAFnsgqWqgpuusnFTTedKHvU1Jw8C/BsME0LwzDHohnOnFdfNXn/fXA4LB57TKWyUuBywd/9nWB01OLFNy1U1STXIaioFXz+HhjsgLVJJ0ePJonHDbxO8NVpdHpVHIOQdQBFCtQIKBbgFDAKIilv230IZldCqgpa+8FyyBWGd98P7QokDPCqMK0KVq+XDuL8CVZuWpaFOANFWnKWDeXFY69D2wiU58hYB2XcL6loVArpmppTB50+9VSW7dstvvUtjYoK+7ecjY3NRcbhhxLbIpxKbIF1kdiyDyJRqCyBtauh2A1FRfLD+OjRyQXWRBSc5SDpD9PUVEFT02kmWE9ANCqtzWxWkEqNPx/BE084+NE/G6zbqIIQ1NQKSgsETfMgkdBQVS8ej8HyFYINfo2DHYLXN0K0QpBJKnDIgmkKdEpRWV4oaO+FEguuvxSSJrx/EByzwJUFfz64VXCOaZEbrobSYugdOnllYDhs8e//nqG4WPDQQw6UD3emnwM9/XDoGDTWw19dAcMJqMv/oLgCePll2LwZ6urgK1+Z+FjJpMXTT1t0dwsWLjR44AFbYNnY2FxksjHoty3CqcQWWBcJvwfSWZm9lFcAZgR6e+WYmfr6j/rszoxbb1Xw+UwKCgR1dR+8zeEQfO8bGg/1wbrNsHYjdHRBfggiKhTOUvnktSrzG6AxAU9oEG6HzsOyOb6t3SJbDr4cCDgFly2BbBym61BdAskMMpohCe4qGNoHt9eBNrbCzrKkiD3WBWu3wV03wsJGeVs4bDE0ZJJKCXT9/PuaDAN+8XuIxWHLHvj2I1AxSWN8QYG8v6KiyY/n8Qg+/3mFHTssrr/+g9EYe/fCH/8ItbXw6U/bPVk2NjYXCHsW4ZRjC6yLxJI5EIlDRy889gA4TThyBCoqTh/p8HEhL09wxx2T/w9UFCgvhZU3wYI5UF0BT/wR4gk59ubZN6G+Gsq9cLMLDkcg4wBnFTQWCrrTkJgFkYNwdB/MzQFHHrT1yOT0e5fB/TfBL56y6OoWrGqGmhA4QzJD62g31JZL4bN+xwmBVV0teOQRJ4GAwOk8/+qVELK/yjTBdZoWtuXLZY/d6VYm3n67xpLLwfehVq1XXpH9WHv3QlMTTJ9+fuduY2NjMyGaH4psi3AqsQXWRcLhgBXLPnjdjAs0w+5iMxiD1zZbPPtrna49FksWC/7732u4XIKsLjOfVFVWmY6PlunqgtogNNVJ0fmDL8P7W2BvN2wdgUUNMNgHK5ZCwG9hGtBQa/HcczpPPWWSSkNRmcpTBxyYflkdPKiAe0iGfB4XVwBCCGbMUInF4JU3ZO/bFU3ynCajt9fit781qayElSuVD9iKigKP3APt3VB7GqdVCMifJBLiOIYBv3peDrl2u+DR+6BwbJ/6etiyRcZ6nO44NjY2NueMHoNB2yKcSmyB9THDNKG1W/69uvTUIuBCYhhndt/RFPzjG/DUv2Xo2mmhmHCoxaSw0OQ//78uPnktPPki9AzAzcvAPxZvMK8Btu6F9h5omg8+H9xwDSw34amXoOUoBHxQUWjxh2d1du822LgxybFjUVKpKKADuby/pZT5N7uYNgsKy+HoEUh3wn4TXgnALZ84ca5r3oe1G2TsVmE+zDrFSsPt2016e6Gnx+LKK2Usxnjyck8fbnqoVVbfFs6C0CkqWKMxKa6qy+XPjt4TAuv2209UwE5XBbOxsbE5Z2yLcMqxBdYUsWUIft0OlV74Yh34xz2zKV3O0HNM8ua1LDjYAd2DsGM/9PWDosKCBrj7xotz/uPZtEk2Z5eUwMMPy0DUyeiJQOsxk56NAlM3MdQs6A527pRLFKvK4PtflMLRMc5Oqy6Hb38eEikoGleZsSz4zK3S8svxw5HDFkePWqxdm+HYMQPTjCHFVRAUH7HeOBte1jh8UCWvFBqnwfImWTlavxmaFp4IFc1kIBqBnKCcO5jJwMGDcvjyjBkfbFCfP19h3z6TqipxytV/kzESgV++IB/3gaPwlc9Mvm3ABxUl0NYFLieUj1vJqKpyBaKNjY3NBUXzQ4FtEU4ltsA6T7I6vN0C//cWCA+DOwQzA7CiDLIGvHAIdvTJD/zl1XDNBONw3tkGb22Rs/He3QINVbBkJuxqgRVXgt978R6PaZ4QVx0dUoAsXDj59kEvGEmwTIEQCchmsCwXcxt9vPSSTFdfcfPJ1Zd0WiaZj5/Ft3UH/PElmDsb7r5DPmfFxYJYzCSRAMvKIqPfPYBXzqrRTcxwkuigH2cOpPuBaVKojU+h6O+HbZsgm4AbV0JtNTz1DOzZI2//xHI51Pk45eWC73zn3L/OCXEip+x0XV+aBp+/Czp7ZaXrbMb+2NjY2EwJegyGbYtwKrEF1nny7Eb4h3dhx/qxKzT4pzgUPih7gbb1QnWubNJ+7SiUB6B+XEUknoTVO6CmBA61wXAfvH0UhnugtnLyqteFQlFk+OnRo1IgnG4cT1EAvvcZwZY/QOt+B6plMH26xq23OXnuOVm1cjnhzjtP7PPqq7BuHTQ2wn33nbAiN2ySVuHOPbDiBoto1MTlgi9+USMW03n9dZNoVAMEKCkQXrA0UOV8v5mFcFMTdHbLgcmVZVLgFeTD7mbYfliOM8oLSgF2oBlqayCZhObmDwqs8yWYA5/7FHT0wLwzCD11uaBuAvFtY2Njc1GwLcIpxxZY58GeAfivzXBIAPNBHAArAWvXwTPXAXEodcuBwIoApwq98Q8KrKwufw6HYccuGNwHw4PQdwBuvwXeWge3nuMHv2XJXirtLF/lBx6Qlatg8MzsqQXTBAe3Oti0SSWZ9LNsmUJnpxROehZC4yzAbFaKq8pKmWY/PHyiv+m6q+GPL8tRMX/z/yTYujmF12vxgx/4+Pa33Vx6qcLf/q1JKmVJcSV0yPXiLdCor4elc+CRz8DQMPzkFzA4DP/nV3DHJ+HdzTBrNoyEYSgmxeOCBXJUEcAtK87uOToTplXKi42Njc3HHtUPIdsinEpsgXWOxDLwT9ulxZU3AL0usCqAdnDkQtaECjdEhiDolhWsjAn5HxqBkuODsgJ4bzds2wzRXnl9NgwH9sGqDecusHbuhJYWuPfes9vP64VLLjm7fVQVLr/8RBNTXR089qisDo1fLelwwPz5sGsXVFV90DqcPUvmgv3dD01WrwKHw4VbxPn6N+P8w/8sIJnSaGoSrN+QIWtY4IXcAoVP3aqxtAlau+CddSbzGgSGKaiugNYO2LAVtu+B0iIZRto3KO9v5e0wf54UWz0D8NyLMLcRZoxFIfT0SEHWMPPkANHhEdD1sxurc7bEkvDSOmhthy/dAfn24GcbG5sLhRGDEdsinEpsgXWO9CdAU8DvhgWzYNexKAnLJB3M5apLwZWAhxvhVQOORgALlpZBw4eW2isKfOpqeOWA7GUCZKnWhExaVr0m4nAnvLZJWos3N01cpaqvP//U9/OhehLL69OflvlQubknn/eGLTAUBss0GBlWICPo6Unz2DfjNNT7uOsuF5pDZ9t2BwV1UFrlYOYM2eWUzZj85KcJbr9B5ZK5HnbtkRlShztkbtXRdmjvggfvkPelqjJX6o134N33ZFP91p3w2OchVAT/+xeQiMKXvwAzxoXBGgb878dlH9n3vyZtzfMhkoaUbtGfhRynoCMuU+u3H4KeZhjql/2n37nn/O7HxsbGZlJsi3DKsQXWOeJUIM8JTV5IOY9xzZItDKdAxOdT65zJoXSWPWaGlfOcWEkHqgJ57kkOpsil/CWfhaf/GfQ0OAJQUwu3LYf9Q7ChB+qDsLhYfiB/5Q+QFeBvh5oymFt78mH9/lOvAJxqslmLLVsMRkYsZs9WqalRiMUsnnwyzfCwxYMPOqmqUlGUiTOddu6GSFgKS0vRwNJlecnUGOhIoGg+lkUVrr3Gz+xGkxnzBS1dCsFcGIzA+iMCV46LfRHB394DN18PLcfg+dfhhishPCorUg4P/HYHDCVgTgkcOAwlhVIoJTrhuX0wEIZmD+TrEPpQ5UhVYe4sWZ1zuU5+HKcjPAovrYaSApgzD/5+s8ULpkWyMEbGcsIxB0VhFXrAE4BcF+wZPKeXxMbGxubMUP2QZ1uEU4ktsM6R8gAsLIatvaBWtnDQ6yM3KHAlDnK4s47ukj52e0zahMJnvKV4T/HVIMcjq2GNTXCvDnv3yDDMh24DqwT+6l1pL7pVWFQM+wcgokFuGvp0aAnD3Cl+fH19sH+/rPBUnmEf0R/+oLNjh47XK3j/fYMvfcnJ6KhJa6uJ3w/r1+tUVakMjsrKn3vc2Jf2Dvjt76XtVl0JAwMW7QkBGTniPT6qkA6n2d/swO8xUZQM/W1wVZOHHQcFu9phZo1gyRwHHf3w1LugOsAjZJ9bMiV70vLz4fkDkMhCwA0v7wfdDwP7oCQAIwkYUWGuF9w3wqo++J+d8FkHFHshoMnIjdtvPvfndmcz7G6BvYcgmQOru2Cg3EJxW2R1C0O3iKXAOwoNOVIkx0xIZcF9muR4Gxsbm3PCiEHEtginEltgnSNCwB0zYXYBPOvxU+fsxeeAlKuAxc4sW30mtZqLbtJE0U8psHwuuL8Jfr0Zyi+Bmrlw10KoKIMfboMcJyR0iCXglUMwGIeGIqhzSItN80x66HMim4Wf/UxWaFavhu985/SVsGzWYtcug9paBSEEPT0mzc0GixerBALyWLNmqbR0whPvQEkefOWWcWGmYynvigIrVyg8dI/Cl78YpS9hAQKhWHjdWfyFkC1UaTumEcDglmuhYTaY74JlyP37wnBsCBbWQ1sMyqfD6CiYPoumpRYvtUBDnsAhBCNJ2OmG/lrI9EPJPKgIQYMF24bl4oRwFr66ExYXyiHdn62EnEmEztE+2NcJM8tgRunE28yohdU7YahG9rvNUAXutELrbg/xsIIR1hBRCCrQVAlep3ydk7otsGxsbC4QtkU45dgC6zxQBMwqgCaqOIQbgUWDVsCyXBfDuOghTRVu8jn9hN76YvjuTbCqHeImlJVDd1y+56+pgDXHQKjg1aAnCgMjUDxLfujOmuI+K8uSIsvtlsLIME6/j6bJhvVIBHJyLBIJi7w8QU6uwte/4cE0LHJzFXYfA8OERFrmVFkWvPMOrFkDo8MwvR5W3ga7WpxoHpVQkcDvESQTFoUFGfaOumiaLsi7VMPr1zjQI/jNeshYsLsV0hkZC1EShByvjLkYzkD/shS7zCi/SmQY6QwR6nWy0KewOS7od4ExHYxLTIZH4VgKhEcQzQrcCvSmQTeh0gPdaVg1CLdPIJ6GovD4GnmfGw/D12+CogkyrcqK4LtfhN+FocoJVxXAYEyQMp08E5GzG3N8cHURuLwwrEKbw+TKX40SUFS+t9TNyjm20rKxsZlCVD/k2hbhVGILrCngKsrJx4MJzCYPBwqfoogEBl5UlNNGTUo647C6R35ARzNwVbm8XrWg5QgMt4I3HxrrodYLswuhsRBmTiCwDrbBb96Aknx4YAV4z6LK5XTCgw/KGXhz58pm9NMhhOAzn3Hy9NMZOjpg0SKNefNV/vEFSMQFjiHB4X3QtAgeuQ0KgzJYdccOePtt2RBfVgZtbTK+4bnVJrlBhUQc0ib4CjXSwsTKWOT4BYWFCr1haO6SYaf5xVJYrVgAhTnwy3dgIALRJFRM13kzmaRNMYmO+HCURunvDPJmiyCSL2AGfxqSaGTlYoP9AZOFbpVCBxyMw6X5UlC7FYhNIjiTGSkey/KgfQgSmcmfL68Cnx2L66i5DiIJcLgzBHdYdA+7MExorIDNUViTgOzeISxflr60i++vz7BiZgin4/wHV9vY2NgA0iKM2hbhVGILrCnAjcqlyPX6MR1eG4RIVjA7R2PuBOJkYARUBUIfus2pyg/xjAE+B1TnyPf8v7wOra1gunWSrhgiq/Clej93NionH3yMtzbJHqejXXCsG2bXnd1jqquTl7OhvFzhu991o+sWmiYwDBnsuW47bP49REfh+Wfhzi3wox/JfY4dk/1mx63CYBCOHIFgrkp+qUoia9EXFsSTMNQGl9TD3jZB+yYZLFoTgHACuofgSA/oSSjww/QSKbiml8KAzyB8WCEy6ic7qCD8FmowTaRXg2KgH1kadwNOMJwwmLEIOWFBLszxwdZRaNWkNXzZJHEJZXlwWT1sOQqLp0HlGQ5ndmqQyRnifQ5Ts8CibrCKGU4f/6PFw7phhQFDoUhNomJhGm58gVHi6TxbYNnY2EwdtkU45dgCawrJmPCLVhhISytvRxjuqYSF47KeXlsP7+2QH9S3XAGXzz9xW1UAvjAbwmmYkw/ROERbIZwBfdSk8IpeRB4YORaBWWlg8hAmNQ2/+TnU1EHOXRfqEU+MpgmSSYsdO0zcAxZ6jyAcVtCcUhBs3AT/+q/w5S9DKCRT148Ti0lLsi6osCXfx8CRBElhSQ/Q6eK9X+pQZKI5YaRcoKYczJyr8Opm8KngLAGvWw5ZdjvhhgXQOezAGc+i9AmsDMRH/QiXgKSAFmABYAB9SN/SY5JMCLbkwJZuWOKWBa6KAKycBmWTrAZVFPjkpfJytjTTiQcnLkVlv/co8Ww7O81rSHsdOFJeQt5cVE8f3oIky1wF5PknF9c2NjY2Z43ih4BtEU4ltsCaQvpS0J+GqrHZgQ4Bm4dPCKxYQoqrqmJpJb22AS6beyLE0rIgnYI97bC7DZS4PMbIflBTBhm3k8SeAEKHX7hi7AvD9GK4a6m028bzyu+g4xikVfjFr+F7/0n2VI1nMCJFiH8KmuTjSXjmXdlbdfdVFs//zqCz0yQnR2BETVQNhKngcAqWXQG6bvHUUwYrVypUViq0tkrR6fHI+YBeH+w95CDuD0CdDoPAlgSQAd2NnoYjBzWGY4KehEaZR8Hnhs4eqCmHyiIYHoVfvwvD0xQW5HiI9+qEdUhpCopLIVou4IgFTguSgB/wgJJW8GhQVwg7B2GXCQEFhvyTi6vzJRcv7QzQMqqytTOH0XQD3pwYWaEwLaedH105nYpMHamMyaxSu//KxsZmijFjELctwqnEFlhTiEORie2mJa2+tAlF44SPQ5OBl/GUjA4IeD6YEL76CLzeAiEPIGBbMxAFYwhIacS3BRA9ICpMtr2QQ+Ml8PtmsKJw9w3SZhtKwNN7wD0PUoegqxuefQMqSuEL94NlWQgh6B2Gf31RxiV8+9MnC7Sz5WgvHO6W1ufray06Okxqa+WDKykULF5gUFEhqKsTVFRAc7POqlUZIhHBN77hYWBIYFnQ2y17snJDMGwAKQWCGhxKAwkgBaMRyA1iCZ2s5aflgMH8uQoZHaZVyfOJ6dCsgyssZxJ6XSrLZqo4YnAsDboTduRDcrZFdhgQlsxfSIGpKzhc4E5AKgbCL4/n+FDvVSwFv980Jiovk71f58p8avHhYnNSZ0NzGUMhN8EcWOpuoz5Sym+NXL5YDAsK7Rq+jY3NBcIujE8ptsCaQopdcFkINgzL96lbhU8Unbjd5YQHboYX1oDPDXdcd+K2VBZWHYGq4IkBz3Oq4OV1cvWglRHob7rkK6aZVJSrHOmE8Ahs2Q5Ns6W46I7CG0eh2YJMLhCFtmPww38zefxnOgVB+O5fq8yYreJQwe3ipBb80VE5L7CiAubNO/lx6rpc+VdWJpvgASoKID8Hkmko9FkcHvcfNTcXLEtQXm5RUTH2eFMQCAgcDhiJwu/ekcL02ksgk5GBoKkM4ALigGqO/SUOaY/MqvDmETdNHFEN07T4xOUCz1g1biQLR5NQqMPn8uH3MSjyQTJXrs5bFYGtx8AsB7UhjUvLkGrzYI5qeJ1SeG7qg+vr5Bc7twq3jRv5A3CgWzbZuzTYeAhuOwdr8DhONHIpI5Ibw+sT5MUsbg8K/svcajqj8EoHBLPnfnwbGxubU6L6wW9bhFOJLbCmECHgtlKYlwspUw56zv2QmzO9Er79wMn7RtPSInSMK1AUhaCiBEYboWULEFfAC0UOhcYS+M6d8Nwr4HFDwVjjdXkAutbBsTXAMOAAyi0Ge3S6sgJ3SBD5bybvvKDw158WaOq4LKoxVq2C99+X0QvV1SevIuzvh1dekbcdF1h5AfjWXbJXaXBAYf0a40/N7lVVFjt3gmkKMhmIRiEvT+OBBxQaGwWHuwSj8bFqngPuugv+15NQUgkdw8AIkKMAJuAEfGBqoCqYySzuIhXdgNGYPBePR1p51+dDbBgCLshJg1uz6MnCjhS4TYGZAdNnEKiOYEWBgBPFVNB0EIpCGlhcC1flQdADFcEPPg8lueDWwLCgZgpmEoZQuM6vcu31Oo1DLgbboaML3m6VDfwvY2/7WAAAIABJREFU98NXrzvtYWxsbGzOHjMGSdsinEpsgTXFCAE15zCbLuiRqwjDSbmQw++W2UtzGuDypbDqEjDDkPLCZSXwpWugtgTqH5XCRKiwfRT2t0BsP5AFvMAMcJVAKqSQ6RRYTotDfou/+b3JdKdKd0eGgbDODVdq3HqtzOsqLZXiqqDg5L6t47c/8sjJ4240FUxh0dGRJR7XefllqKpSyM9X+NrXVLJZhbY2uf/y5XDggMXoqMXiJkF1qcAwYfZ02RdWNxNmXwI/fAKiUQFph0zlTKTA6QLLAWlA19AdCroueH8rpFMmoYDFrBkCT1AhmYW8HLg/D346ACM+i4QrwSgZgkEnjtJuZmb3c1BvIKHlYoUsrIiFqkGhBuv6oSEI01zQ3iNFr3tsPE5FPnxzhXydzscePI4HhbsJ0ByGf3oJMt1SMC9aDodVqA6d/33Y2NjYTIptEU4ptsD6mKAKKFbhpxvA5YCykIwYuHMuuHJhKCC3K3TCV6bJWYggM6sA3h2C14dguBdigFDA8gEm+HsESo5CItfCzDMQl6VYXWjw9OMe+rYIfBmN5/8A6b83ufMWhSVLYNo0md4+0aw9ISa2DgFWr87wxhsZqqsFoRB0dRl88pMumppUjhyxmD9fjt/5l3/RSSRgzx6LwkLBf7rvRBnNNKEwDyJRePgWePYtwUhUQ7u0FKU/SnTUAagQ1SkOgNMUdHRb1JRZ9PVaDA9CR6+JUSSYP1vwk/fgoSa4tBhS5hADjhFCHkGNL8swCbZFF5H2eLBQsLKCtBO60xaaJWiNws/3wW/2QfdhUFT4p+/J+Y8AeRdg1uMLu2EEk76swgwv3NBgck2ol0BFMfY6ahsbmwuC4gePbRFOJbbA+pjw2mZ4+T1QE1BQBFoGHl4IDSXy9i/XQCQLdT4probCsKNZVlTmzoD2FOQ7oLgG1tdC2CPT2J1RMBIQ3q2g+k2UXIHY66F3OEv4mgyZoz6MwwlSo/CjHzq44lI3+fmC19+AgUF44H4oPEP7yzQt3nsvS1WVgsMh8PvB7TbZs0dHCI3nn7ewLJg5U4o0y5L7KR/61qQo8OAt8NJa8HnhN/8DykqhuUPj9dVBlITO/gMGLp+LxpDGkXaLzccsdo8KUCDfBb2jsLBK9i15VXhlH3zqap039Ajllhslo9KrK/RlCsloHlyOJMGyYapHOjgwMAdTcxPwCoIa1DrgzXbwZCCdhObWEwLrQlBXl2FDZZh7Y3ncN8uBu2M/7t/8FO54iHTDIlr7wciA12FRXiz72GxsbGzOCysGadsinEpsgfUxYV8bVBdBvAviYVhaC9PGWXA1Y9EPu1tg/Q7YtBtKCmRj+BfuhCUF8EwPmG6YezMUH4DRBKQGoOstSKngTArmTLOoWKKzY59C+oADkiaG4iQZho0bTe65P8Vvnvawe4/Mozp0+GwElmyAHy+YVBXSaWhpscjNhbw8OHzY4tFHVdatMyksVJg37+S6dCgXHr5N/v1oH/yfVXKh375uwaJaB1dd6yAYgD3bYf8hA58Oi+eZLLtKAcPk2Y2C4R5BrB/KB2HmUpM3RJKIQyWVVejv8jAQg4TwgU+gGw6Cnm6um7WaWLAAo7cGBViYC14LSmvBaINrF8Ll86Q4bOsHrwuKgied/nlx23SNWtPHXEXFBVBcBkuuoj9Qzt//rpPV2032tJShuSxWzjZ5/PsufN6pPQcbG5v/gNgF8inFFlgXiEwGjnXJaIbaClmxASmIXhiA/XG4tQDmj1l/i2bC0GYoy4GZ1fD562Skw3i6++E3r0HACy2tclVibkAGkl5aA9+qgaQBP22Dy66GbbtgbxSyTtCS4A0IFhQ5wIJgTJBo1hhMWrJvXA+D5WDt+x6aDxhcvlRlaAgaZ535Y9Y0waWXamzenKWyUsEwoL/f4u67NRwOQXOzSSQimD9fUFmpcN99JwurcNjiiSdMiovhnnsUFEVwuE+u1CvLg/QCEAn5HBXkQ2cEPlOnsLfZ4rplgkfuUdjYrPCrLdCZkLmh7Ucscr+UYI2RRhUqg3GTPfsN4j6/9GZ1BXJNuowyfjz4FQIpFzmqwGXA9qPytRsNwfdvglvq5Xmu2g1v7pCxFF+6CaqmoMn9OG4UFinjGvmCIXovv4vl3xrk4AE/vrIIlxRvZofVxPOKyRda4MYFU3f/NjY2/wFR/OC2LcKpxBZYFwBdhyf+CK1dstJx5SJYMfa+7cvA5lEodMDLQycE1rXzYfqY7VRZeEKQjWc0JiMV8oOwcJZsul4yFxrHRtqEHIADbmuAn70HRzth2SyosWD/PgjkQUdWIdPsIpQ0iWYzRJ0q6bglQ7nSTjAEP/2pxa+fkefQ1g9KQg5OPhMa57t4c5NC+w6d+nKTW25xsXChA0URFBUJkkmIZ+DJ52FGDSy55IOPdWAA2tstBgelxelyyZEzySz0j4LmhPuvge07Zb7XsX64+SpBnSZonANHe+CPG+D6S+Env4dw1EL1Wbz1nKDuy5DIOmkzkqQUFSsswC1kSnxSAY+BsFRqUTB0QXsXzMmVYa++LPxhExCDy6ZD+4CsXsVSMDh67gJrOCxXgXomCTCN9/ejulx881887DsUgmIIu3wcqvZBRmAUqrycgKsM8NjfPm1sbM4VKwYZ2yKcSmyBdQHoHYS2bpkobhjw/na44XK5Mi+oQYEDBrOwaNzKMyGgqmjyYwJUlUrrrLULcvzw374Js+tP3m5RBRQth58lpUi58VooLpVZW8eSMDoERVhYWZPGSpUBFDpHXOBS8XpNigql4mnrhx+/CA0VskpzJry6RlBe6SSQ5+ALD0JF6Qn1VFoqGBqBn/8cAj7YdxAKQlBXfWL/ujp44AGFvDyBZQl27oHSYnhwGRzuhbpi8ClwqA0WzJRp7Uc65MihkTz48XswnIR0N8QSFpZikU1Ay+tOIreMknGD6lDQizRoFRABjo/Ncat4QxbXlcORNjlkuVXArCA0OGHTYShywqE+uGsR/OxtCAag5jSv22RE4/CPv4S6Kvjspybepm3tWnRPiPVHrpGVxgygQVz1gYBaoeDPg6huCywbG5vzw7JXEU4ptsC6AHjdUjDFk5BKSxtPVSFuSkfqryogrEOx8yyP64HH7oXOPsjxQckpqiZVhfDde2Vq/K4WONgK8+rhkRnwNz8CU1VYOM9FflBhxCcYLLDoGbC48SaVv/6q/F9m6FARglmVpz83y4LBGJQWwc59kBsQBCeILkhn5La5AQiPyn8fJ0EETXGycKFMC31zFTzxHIyk4D9/B1YultsNDMs+r/5hmF4Oj90DnW54rhe6k9A9FlSatSwsARhgCUF41IXapiD6FdxlSZK1GqYhQLdAF6hZBY8PFJdFYkjgTYJIyWHSER1cqkzgP9IHkRRkVTl/8reb4IvXnpyGn83CT34FSxfBovmchMcFC2dDZcnkz+uM225ja6uKnlYga449iWDt0yifrbByGVR4x6qXNjY2NueK4genbRFOJbbAugCEgnDvCnjtPcj1w23L4fkobEtKi+86H1x3jsv7vR5pre2Lwnu94FZgfwR+t1WGnv+XK+C6sXExfq+8XLMIKoohFICfPA2zaqGtV9A9qFKYB8EQlJYIHl4gWHgD/GIvtK+RKxBDsrB1Wt7YB2taoMAHD98FpQXg/1Ae2JsboHsAFs2FHfthXgPU18jbogzQzGpceJnLTegZFT0Lx3qhtAxe2QxLZkKcOM6Qzuc/lcv2FqirlJW9VZ3QmoJEEFJDkJlmoZoGeqsCloW4IoN+xAEdKpalofRnyQmF8U2PE0/noCgmTt1Hjt9Lt5llKKRzeUhl83oX2ZTA6wY1C1uOQH0xPPm+dFULA9A2BEMxKP5QIKuqQl0NFH0oL+w4mgafWn7q51VzufC4ocgP6ZDCUBKUNCyugLlBuEqBGytBs7952tjYnA9WDMuwLcKpxBZYF4g59fICckbx5ijUOGQW+VtxqHHCtLOsYB1nd1SuGPSrsG4UtrZApk0GXn5uEN64DxoK5LYJ0nQ7RghMc6CF89B1hQWNUF4MQyMw0CVtLs0B190E/7hOVr/aemW/U10BrN4FSxpOfU4tvVJwDMYhv0BWqMZjmrB+F0Ri8I374Y4PWY4CFYEglXDSOiR48Y/Q3wezqyBvphRXSZJsYgM6BkJZzC4lxJ5u8IQgrsP2HtB0OVAnHbMQNQaiwQLNRORBdrcLy2eiqCZO1cCTynLV/LW401n2Rq/GYeTgFhnKiqM4vIKWUZP8uJe5yRzaR6EzBg3l4A5CWwJChqwQOsZex0RaVvGae2W8RnU+rLj+3F7j8Xjz4LZr4O014HHI1+WGJdA/AvUBOcbHxsbG5nywBJj275IpxRZYF4F+Q/YNKTKmCQWIGKfba3K2RqQl5FRkoGjaADSwEpAyYG2HFFhZdNbQTJw0Jib1gXKKC6vo7pOVLVVYWFnwOCE3KHjmLdi0C1KNMGTA0LCMWLhvklDR8ayYC6/ugQWVUDBBdU5R4AsrpcCayBLzE6KofwW/eMpJe6/C/j1w8zVQBHzzLijMhzjmn/68fchBtRcyBrx2GDakIXoIMjHIZEz0KDCsSE+2xIGlCpQcnZxQmGD1CF53lJzCUVTVS76ji9xAjKQVIaIaHFIEoiBBQShMrNTk0Npqdo8UgKbw3DEFp09QnQdVlbJHizSs+DmkknDkEHhGIb8RHrhOPnczC879tdZNGSB7w/UyfDWcgOkF0NMHV8+DmRXnfmwbGxub4wj8CM22CKcSW2BdBCo1WGtCyJLtPhZyDMu5EtCgPS0DNE1kmrjhAsUJ3hxY1QOKF26sTZN0ZiggQBadXnWYh++o4t0Nsv8pR7OId1n0d0NVtcLb6wTF+WMDjHPhyhIpjErO4EO8vhi+XnzqbQoLIDfv5GBRkBWunzznZU0LBHJhxIRfvwtLrwZ1bICzDx+LWEyWLIc8PgbjMBiB/Udh7TEwE0DMlO9qHfBrMBvwgxIyqJp+mGDOMB5nkqTDg+W0yJgBIko+PkeMZDqJYSr0G2mcYohi1cPgiI9dh0PEBwXEBfgtEpYgnYDDXWP26Qhk4pCMghGETD5kh6B1GJ7eA99aCsFJVgmeDk2BhypgzT7oS0CuB9qi8N07oatfNspP1OtmY2NjczZYxDBM2yKcSmyBdRGY64abDbkqTRNwdw6UObKEaUagEKABZZKXwjBhV7fsb15QBk5NDjE+lpSRD4UqeCtlxSmtSwG3oAzaYvDqYJJIWRidYfJxs4RqcgKw8gZ57E2boPWQRXJIsKsZphWCIeCv7oTXjsGMAmgLQ47n/B5/Ki0b7Z89AK4QfPUyKPJZvJTV6TBM7nE4+e9/FPx/G8AYAvrBq0D9MmidCf9rI/xgGXidECSPZBbKvPDketi9DtIpoAAYNWUjGsAoMA/wAKpFrjaIqQlipp/kqB+PJ4HpUOkUAUYEOC2N7nQOsYxKeWELcUsQS1u0byslGdcgJcAPmAKiMm8MN+CQ4xHdqoxzMDJg5chRiXX5cuh3PDOxwEqk5U/vBOOIxpPrAJ8LdEPGQuT74bV1sG6HnNv46N1QPEmfl42Njc2ZYk707dfmnLEF1kVACLjGLy/HGWIrI2wDwEInyCUT7rutE36/WzbHh5Nw40xpD36tCvozsooVNaFjmk48m2ZNqxu0USKOGBnfIAupIMIoCUxy+GDk+PwlFhU1Cj/4r4LdBwSL5sGsOrh6pkHryAEOdaSYUzeLpsrJQ7COdkLPICxsmDjLybLg//ohbNkDowrMXATWZXJO8xbdIG5ZPNWR5ccJMOarsFGBOCRCcDAtWGJBVxw29eosLA6jHHiTH+9v4N8PLKR/G2TaLZgDZC1IIQXVKBACTFD6dYLeYerKj5DjGiKeCDCQKiI8HEIb0PHXjqIHfaQtB31pD+Xuo4SMQZK6m3B/PlZagYSQCccW0ufVkMO04xAIQNyQd+/zylmFigo10yFpQnUuFE+yoOHxVTI649u3Tfr0/okr62Xkx2gKrpkpV1cWhaBvSF6OC6yBUXjmfSjKhU83nbyy0cbGxmZCRABFmSqL8MkpOs6fN7bA+ogwSKOgYWFikJ50u2R27DNdkZWQ47hVqBqrLOVh0ureTYpRZogYuzpLsIrCFHiH6cnWMmCqRBR43BrkUS2XCkXhEEleEyO4ixWW35ZPOuOgrgbuWQkpdR+z61ZRWahyyYwOPKyc8NxiCXj8BVnBSSThE0tP3qazGzbthOoyGByBvFF4/VkYGhI0rdAYqcvwb4k4VtCF6FWwLjVk6KcTDH+SAdcol5RpPN5tcCRvP2VHtmEd7mRo6zwyrQJ8Qqo1HSl6coEk4ACaLVzONCncHIlMI9BfwKVLthBwxjgw0Ihm6iRGAmQCDpxKhlxjgBvcbzCSDtIjyskmssR1gd+fJZ7SMIyxJrocQJWxVLOC4ApKq9LnBOGGhRWwuBzung+1eZOv8Guql6G0Z4JTgxvmnPj3J5bCs2/J1aHTxlm4LT3QPgRdI3BtI5RM8RgfGxubv0wsYui8/1Gfxl8UtsD6iAixCJMMApUgk3eRL6mCkaRs5l4+QagogIFBlCQwQHnBUa4tUNlm6ryVNkiwkzazGpeWpUDE+VmqmEc9ghdFN3FMVNzctDzNg8tPBCkNE6G8xEV1SQ7/P3vvHWXJedb5f94KN8e+nXP39OSkiZJGydIoWJachI1tAcYBWDAsXtiFhWPOLmd/sAsHfuzPP7O7YGOcEzayJQvJRrZyntHkPNM5x5tTpXf/eNv2yPTMyLKy6jOnTt9bXW9V3Vt1e773fZ7n+9gsXfDcAiakEyqMmU6uvE0yDs1pGJtReVYJHc6ehYYGOPgtg95PlBGejszrSAlaVOLZIAwXKSBXMBnaOEVwNkNWS/PQwk18+wt7qM5IQCghNQusVU/RULNNVaAO1ako1KEyHqWWjXA8tIWdVzzLmNmDRRCpuRhSRwBrAqeoOyEGiwPUEiEamudZNNqwQmGSaRdZM6lagNSQUr3mPR3gzcHCrGqLlIpAVxzet0WFWEHN4g2OKyG6pgdCyyHBnasu+NZekq1rYdOAymc73wl/Qwccn4DmBDT5uVk+Pj4/A562QgsRnxeNL7BeJQxitHLzJbcLm/CuTRffJoDJLtYxSoEgyn1Us3Ry2GhCQ2pQ8wzmPJtZpuiSOTShM40GlKhhsuRG+LOizZOOQ7e+kf8Yn6PFmGHOCvDt6j9yvLSXHaKLO5s1Yst3TcCE33ovFCsqXLUSiQT89SfgXx+Fvh7QLHj4IajWJNJUOWmdrQ4ypLwOPBeVue+BEbDxIjXOFXSu1qOEpzr46v/YBEUNWlAzV4vKJJR5oBMVxutFia3C8ozTLDCvU9GiZHvTFDfGiZhlXMNAJF00zUZIEJrAc3RsI0DNi2C4EG4IYs3qIFySQQ9HCkxPw5CqpPmRU0pAXTsA04tw82r42NXQk/7Je7D/uJptEsBAN3z0jpVbIf2s6CuE/xpi8O9eAmsIHx+fNxeCGAZXv0R7+9xLtJ/XN77Aeo1Td2G+DnFTJTtfiDYyJNjGWcYZ5zQVzaGzXmfY7sYKmFhukJheYs61eczKsztUYRcNNJFmlim+XUpyl12nJiTnHINs4Xo+FZvg0dr91InRGX2YbxV/EXsixG/1CH70RSccgrqmZrH0C4TCmvss7uj2mJ4KcWYaIv0epzWblhttSp7GfHqe9JoauWID3rSplIguCUbqhNtz1BYMZhvOcOiHDRDOKCH1o/eiChwFXGAHqhpgtAo1C3oTENDVNi6wpFGejqNFPMINEhEQmFGLsFFFyChOqolAfZD+wBCj3gCl0gC9fYKKVmHsnEEk5pDCpISBZkAaiCzBLRshFoSJLOxd+3xxBSpPLRlTBrTDk8oN/vxG3sUKnJ6EoAkbulYWTj4+Pj4vJx5lLJ56tU/jDYUvsF7DFGz4zDBkl/OwfrkL1sT/7XbTRfjOaViabyWc6iHR/wTRmI4hQkzXOvE8g3CogotGpRTijBmmw8vSwjliJKmLFOXAA+wwbUbsVUw4PUx7ksfqs1QDFSJagUplM6bpcLRSp+CESC0LhKEC/O8zsLcVbl3BziFLnbu9UQ5OuxROtjJ2Os1Qwia2yeKjjRpD0qGuL7FzzXGO5LeSj6SgDqFglWDYojIfJzxQop7KEe3dTyx9J6V6HHIsd74WkJHKJc8EQi5sNqGgw1wVUjGlhIpqe6+okw5DqUEnYRTYIiw2ijpjlJkxMqTDe9nAEoecMOOlMC0hl9JGWN8Ci4NBrJEA7SFoaYJjo+CYcHYaOjLKs6p3hWq+bevg2FkYm4IdG54vrmoWfPr7sFgAV8IVa+HdK+SyvdwUSqp5eGsjNKYvvb2Pj88bDxe/ivBCCCGuBlZLKT8nhGgCYlLK4YuN8QXWa5iDOViyoCcKJQfunYHf/ymBZbvwyadhaAn2PwGd6wbooYrrGgQGQowUNhE1FzBqdbL1NNVClExkjn3BIOvkDK52FkfGSdLOnBej3zjHrNVMm5gnK/ZRkAY16VEPTFCrjzEn48yKCCkacT245xwcnoA9FwgRFrGZq7nMFAVL9TrjeTBjgtmi5OExGFhdx/UkoaDFqu1nyHWkqMzG8RydQLJGrK1AJRil7hq4DTq93cMcq25RGeZZoILyR0gDC8sHzQhoMaHJVM2cA4AAPe2xrtfi90O3ck9gEY0iccZYRSMRPIIM0kA/NVq5jSj/7OlIW2OrGcRLBFjTIjj7fRgPq7Bgb1pZMWxfBUsV2LUBjh6HRwrw3ht/UlW5phf+w69AtQbtP9UYej4P2RL0tqhZwINDFxdYZ5fA1KH3AjlvL5RjZ+HQKdiwCjYOwN9/E7IFCAbhd+9UuXU+Pj5vHiQC6QusFRFC/FdgJyrb93Oor/NfBq662DhfYL2GEQI1SwN4UpmSn4/nwdcOw/4pOJ6DbBYWTvYyW3YJNJWZNrvRO0wm57oJmhU8TyMQrGPoDq6jcyC7jsl4A33BIYLmINs0j5ybwtPW8wfmv3B3SGLXTPJOIwEsQqUy65IelmEBjZRtmC3DtgYIC2WA+c1zKpz5/tWQDEIHEborDexftKlORXDMCuGsQNNMSgmPGz2dJ6kzqbWCBtHWCtH2snr9UuKio7sehu2RaMgheyWcRuVfZVCVg0GgGxUK1E3ISQhISGnqDi8DC7B+M1zbnqE9IGmgyiCTtJMkTIRewniezqg1iT61mkcfaSDiaNgbwO6GvgDMHYOzoypJ/WO3wPotypvroWMwMgEzM5CdgOFZGKvCbbthW4+6VpkLVPOloio5fj4PVQv6LtL4eb4CnzmsqhI/caWqWnwxLGTha/8C8QicOKeOny2qSs+RKVjM+QLLx+fNhkaUAC/V9PnfvUT7ec3wbmAbcABASjklhFghnvR8fIH1GmZ7Cg5mYbyixNUHe57/+1wNnhmErSnYtwi2AHdBZ0z0snbdYZLxeVKhAhNNLZiOi6Y5hIwqIa2ON6czNdzLMW8dN233aIws0scwV3tD3B6JEBMGeYKEAxViTomZSivJZI6WzCAO2wFIBOGdq2CsAFe0wVfOqDDZWAmenoVbukFH44ZgC/8yXqZUL5KIG8wXA2jDIX5zp87guQjRaJVaawxXajjoBDwL3XDwXJ2KGyXsWXSZWTKJPKPpbmWTkF1+E9JAF2qWykYlyAckYaoE0haFUpzQ1hq97TpXhSNcsUowEqmxhI5LiiPU6UOiWzA+GKcoPSqHG9g3pBGPQOIxeOc7YG0X3NsM/WthxIMHFmF2DHb1wHOjsLoJTk3BpANjFhSOwaEx+NRHoO0iIbd4BD5yIzx2AqIh2HuRtkTJIGzIQNCA0M/xybUdNQMXDqnWRdEwbFkNR85AT9vKrYx8fHze2HiUqfLMq30ar1UsKaUUQkgAIUT0hQzyBdZrmJgBH1sFi5Z6HPupqzWzBAdPQL2q5iu9HmAG4sklwkYFs17DkTqN4TlyToqm8AyhWoXycIqzQ+spL8ahCKOxbhq3LuK4OlXHZlI7QyB4PWusEzyhDbAgU+gBkztjJYIyyGF3jB7RT4NuckU7XN4m8byDNATKjBU68EQXqaCJi8c4cxTjRfp3jPH4wwPkCkE8Dbp3LLG4bol/vn8NI4c30/bLx6g1h7Edg7KMIR2BpkFSq7JTG2JvMM89zvuo9YbVjFQ7yvuqgBJWoCoGPcDWqC5GiPQW6F21SKkUI7a1yELahcYkxwiRwSBDkiNMsQDMTEBJy9JodWO2aiwswegcnMjBF+6F3ib41XfCmTrk5+C0B88chS+fhNwSjMxDqazMRidm4cywEkKP7ID3vwUKRXjygJqV3LMd4ueZj3Y1wZ3XXfp+COjwoRfQF/JStDXBTXtg3zF4y27o7YC+TrjtWiW2fDNnH583HxLw/BDhhfgnIcTfAykhxK8DHwE+c6lBvsB6jWNq0HqBPnapqGRti8fBMUE4rdFUh3QfEJJUFyOs2XiEQNRmzm4lIqp060M8OH49hVwLnjQgBZhw6vRGOjdO0lcZpF4PoA2XoO1hBhr62Go/Qs3tZiiWQpLkoWqaol7hkcoif2mfpNk6gxcN4MVO8faeJjojcSKBJrY1vYvDDDHCDEVps6/Lo/m6aerHJEGtjrGmzlezHjNlh/Hn2llaDLHqrScIba7jRDxihHl3LMPbRYZx1nOGCv1mBNPTcQIeWJryu4KfCCtv+TGAIVicbUMPTdLSPkfzgI0TgEc0iw00UCJEmBjNNKNZY8xUHRrcXoLZtQgBV25UeVEP7FOuEFOLcGgahqrw3BJEy7CmCTwbpkZhwgM9BBsTUC5CfUjN5h0/AvI6+MJdMLeoZo6Gx+E3f2nlazo/D6OjsH37yyt0rr9cLecTf0HfyXx8fN6IaMQIccVLtLe/fYn289pASvnXQoibUF/p1wL/RUr5wKXG+QLrdYqFy9FSQz1wAAAgAElEQVT0MFf+co2b3BBTYw08NGSilQ0yGw7h6ZM0JmaRpkeLMU+5HqUpOEN0dZVvHL8TYi7WfBBMQc2NcGZ+gLFoGx8f+gfCZpV1Z49y+sp+1ke2YhHDFSEO25AzXIQbpVv7LIdLh9nEZbSUH0WYO4mEE1zVDtI7gBTXM8YsGRI8mtN5YiJD4QspyOrgScxjNYwbbIyogwhJ8vkUB762B/EtiK+FOwz4wJ0arU0Qw+SuWZvxmSDRoI6TFDi2RFaX29YIlNgylx97qDCiJslWMnQki9iBCoYbZdYBgzofMJMsiArb6cKoDrD/DITTUK2q3onRiCpArNrw5GnY0QuPn4Gzc2omyYzAaQm5s1BdhGgbaBJmsuBOg56DRAZy42BZMDMP3e1KYI1PKwd3Y4VP34ED8PAj0N+vzFh9fHx8XglUiPDZV/s0XrMsC6pLiqrz8QXW65QpisxSojcWY4yzDGzIkmxwePJsB0HhYkibtvAMFTuIsCVdxjBJvUg6lmN2TSvH8luZrncgHZ2m+hzNzhydsUm8qE5fYYhiJM6XCpfzYLnMzoYDVKPNZElwzuqnWY6S1seoRZb4wtAM78mfJR1cIta1C1d2oCMwCREiwFLd5e58C8VvJKAWgKAyBrVPRAj05ajEJeFrHbRDAs8QhLs99hga66M6ieUwWsIJMDMbIxC26EwbJAOCGoLytKCAhpcQSlAZQBSVj2WB3uEgDY1aNMCS1UADdSLSYMIO8JA1w+pollG3xrZQHwIYGoGjJ1AG8Qa0dEIkBLtXw+Yu+NezMJCBuAtDIaicBlkDoYGzBA0tsDGuPE9FDNImXLlNVeZtXguHTir9t2PTyuIK4LrrYNMmX1z5+Pi88vg2DSsjhCiioqig/ocxgbKU8qLlQL7Aep1iLn8QajjYcppV06fZXRznuibJmZabWExOUTMtGt15wsESYVFmiUZCos41kUeQaOSX0gjb4YqtT7K6ZZBmb4bSpjiFUgPftW9n2jKZKjZw9+CHuXH9o7Q1jZIxAoxYHRzX1pOwsvxi8J8Jbq6zIF3K1j4s8yhn9D/krSLIemnxl4NzLNKGrBhgAQsCmgFNYE2HMHWP+OoCvbscbmkNYDRVudlOscWIEFxuKWNqgvV6hCFXEgjbRDHYutnhmSdDOAuSShq4RuA5wI9zmzykJkkaNlcHwjxXM2gyawSFIG3HGfQ85hebmStGuSUOm9vh/3sQOpvANKFcgf2HVIPtcB1KS6CFIRaAy1rgHyYgb4DZDok4GIuwrh3qExBPKK+rPTvgjjvU2dxxi2p5dGYMursvfF1DIejoeFluGR8fH58LIogS5vJLb/gmREr5vIpBIcS7gN2XGucLrNcp7cTZRhsj5FhrZ0gUBymFG2m0TEJWlsfNRqRrEhNhdMYIUMaVBhLBgmxkdfAsTxjXcnX8cT6kfQkjbzOZbiOpF5lOtjJaaiWXSzGXa8VxDe47dT1/nPrvGFqZcaeN/fYOhA6bm09QjYYpyyhlq4F1U3VazaeYaZ3hT59s5csP7sXbbSGiLtLSoFkCGrggMxJn2sQMajgtVTzdIxKQdAYMgue9VlODj/Rq3D0VpSdt8YzlMHFcYgqX/u0ek2cD6EEBSY1FB9wSKgF+2EAmdZ4+ECSUBtnoEUanrhkMhCQnSx47IzFOF+FdbdCegqIFWOA6UCwo9/VoGIanINIKi1MwNA+bGyEXUflxAQ8CJWjUwIvCui6YmwMvpPoPhkLKBmFsDnrb4buPw84NKsfKduBrDynD0Q/eBKEXab3g4+Pj8/PgUaHMvlf7NF4XSCm/I4T4o0tt5wus1ykCwWZa2EwLnt5F1XgGUasR8gwSod3E8glOOaeYFwEak+eI6kVMcmTdMEJ45L0EwvJ4kmtYKmfYGj1AmzbFlOzFJEHZjdDJOPN2K5qwsd0ADoLL5bM8xLVM1ruxZYBbQ98jGKjh1g3azo7RvjCPLfL84fQ6vvmDa3HsIDxswkYbyh5UNKh7sMVBFD3sSpBwBQaGBeVRhw/eFsDDYx6LJn6iNnqj8PHVGtPFEEf+RTI8I9HKLvFxg9vvMJjph4gHR07BXBrqFQ3rDGTvgXwX9F0Po/0aE2HI6PDr/Q14mvKWCuvQ1wi7ByBXBjTlzj7QA+0ZZZER1EGkYfcaODULH9gFu+bh/ufAmoGr1qi+ij3r4MEp6O6EWBqm55VIS0SVt9TEPEQTcN9+aG9QqWOf+a4yGd22Cnate9VuqX+DlHB0VBmhbu1Tnl0+Pj5vXPwqwpURQtxx3lMNZToqL7D5j/EF1hsATY8Q7f49yB3geLDCyaTBkclmmk2DAlMErU1UwjYFsvTp53CrGg9Ut5LI5HA9kymznVRgngP5HeT1KM2pAoZWx8AlTIWiEyfYlicoHKQWZDPHKJTSeK7Bn5z8cz684XO0JSdpq89gZcJ8LriXx568AmktO6MWNTgSUA2au6Uy7Mrr2FaIQBJaw4IrFiPMjMDJWxcZ0ioI4AYa2PiTmB+OB58/DFVLkJQ2Q3M5kt2CjvYU0YDJwQkoaBBwoXQEOK2BBO8UjEtoBN57JTxnwT0F2NoKTVKSi9Y5GHS58x1hvveIxokxsE1Y0wmHzsHObljTDgNr4FsPQ0cSKrNQOApdi5DqglXtygx2bB4+fCf88GloyyjzToBsGTKtMFGDfSPw2WdABuH6tcony/MgGXkl75pLc3gYvvY4GDocGIaP3+ZbOPj4vFHRiBJl16t9Gq9V3n7eYwcYAd55qUG+wHqjEGyCllsYZD8OFl2JCsOzGwlqG7khBQbzHOVJGqmwLpNHVP6Jx91d1AnQGpgBDRZLLcglg1IqxGWxSYoBj3YtxaKbYmvLUZp0jbDeTCJg05maJL+Y5PjiJj7x0F/QlJ7hj9Z+ki3mBIcq6ym6CdyyBj+KXOcFsWiOaiWEi64c10M6rS7cloeAK3BNl2EqtBOggsdxSs8TWDVHhfCu3gIPfs+hKS7ZmJFQcJk5bBKcgegUJNZAtgZy7fLASdByMDcKn2uAcA+0CXiyBl1Bj4VABRdJR9rgo+8K8vRpuPtZsPJglKEtDr/2djhxBpqqyobhy4Nw7Q4oluHx5yAolE1DQYPHZoAO2LgJYsuzPg8cUoLlu8egYEHJArcGB6bg794LmQSs7nolb5hLM1+AgAHtaZhYAseFgC+wfHzekLiUKfLcq30ar0mklB9+MeN8gfUG4yrWMk2OvngzxSBEdEgYsIiBIMUSzTRzgndFltjhPcUSKSpejGerO7HLQVrCM7iYNBklQrpHvOMwSI+tYoxWLchWeQ13VVqpLATJLoXRpiSWE2HS6uU7mbfxOZrIRTLYkTC2HlQmoDYgoFqIEQ6UqbphXBfSEbgmJUhKwcISrOvRMDWDWSxsJDtIUK1BMKBmTqImiCT8cAEyN4W4fq3D9g5YvTHIl4egdzs84MBTJQjEPRzhQcBDrJNkKgH61gtO6/ALAQhVVXjuydMa3S0hOtdZtAn1cVjXCV/5Huw/oFzNJ6bgh09CW6MKF/7I17RWh6FBoAaHjkGyHbrWQ2eDCq89eBL6m6C3EVa3w5kp1eEnoIPmqP7UXg22rYFY+OLXNVeFJ0ahIwGXtb9st8/z2NYPB0eUuNq75flNqn18fN5oiFcsRCiECAGPohqdGcC3pJT/VQjRB3wdaEC1pfkVKaUlhAgCXwR2AIvA+6SUI8v7+mPgo4AL/K6U8vvL698KfBL1p/4fpJR/sbx+xWNc4Dw/xUVCgVLK373Y6/QF1huMDHEyxEFAdDlT3MLmOY5iYGCxC5tuGnmaiBDMu5KJWgfn8uuoC4MbWp5gXnbRUsrRrCUhFMXWy/SwDYHB0Xobo+ON2EtJxg/Eoa4S14NmlRnZQltqmjkjhRUWmD0e9pCmLBROgNtsUIomQUCDgK2aYFNC4HnK6fwXrxS00My941UGBw0+81iIPz4Nnc3wV38ARlolkd8ShTlL46N7EzglODkGS2UIL0JbuySccAg4dbwqNMfmcYMGhlPn9NAA9gb42hMQExCtQXVKUGyIkLEjHOyEa1Iq12hPP4QcGJFwvAbTh+AfPw7nFuHQPESL8P0DKhH+3XuhuQH+9aRqYaMt95DUNTg5Cf/0BFTq0NUMO7fC2UGwPdWq5rZrLi2uQFlEHJxWNg+dSWh8BfKhGhPw+29Xifjh4KW39/Hxef2iESHGzlfqcHXgBillSQhhAo8LIe4Hfh/4n1LKrwsh/g4lnP7P8s+slHJACPF+4C+B9wkhNgDvBzai+nv8QAixZvkY/wu4CZgA9gkh7pFSnlgeu9IxVmL/z/MifYH1JsBZ/hcnioZGgVVs4g7y4jHOaUeYDNi0NQ6zS3+cGiE6GWXciSNqDmsLFg3NXeS1PJ2yiweKOQw7RX5ag5IGaUlzZAq3X2eu1sRkoRMiAr3PRtMgoDvUZkK4zcAYRBphoF3QEhP0xyEkYWQMLtsCfb0wlzU4/Gic04vw4Gno8qAyAl+6Bz72EVVR6KD6IJ4cg4ePga6D2wQ1DcR1DoHBOoVsiKhTwq4ECOg1Ghvnee5cL0HLgAZIOFDwoOrAjAMlHX6wBAUH3t0M6RZ45FnIC2hwVWub+0/BcQda2qHHUFHOc3VIxcG2IWGqMGa5DnN5Nev02EnIRFWe1WQWLu+EjhYlwuJh+MhlL+waZiJqViwe/Pn6EP6sGLpafHx83th4VChw8BU5lpRSAqXlp+byIoEbgDuX138B+FOU+Hnn8mOAbwF/K4QQy+u/LqWsA8NCiHP8xD7hnJRyCEAI8XXgnUKIkxc5xkrn+YWf53X6AusNzjzTLLFIhgQL5NHR2MYmNII00EVcHGGLPE1ej5CgQLEU58zceqrZKLYwmXZD9FUka5okC+VZyja0GFGemIyDBZHOEul1iyzONuGIAI4w8RwBmiC6ukZPh8bmukXYSjExBgefA7MCbWFoqCvn9E3r4cAhCIVh3XaYrMLBHFSTMFWG0DhMzkLKhI90wskSDIThqw9DR1rlCXWklICpx6Dk6RD3kN+FprfOEGytk5tKQ1LDGZQEOgS5JHRWoGUL3LoR2lLgSjhcgr0J2FeC5g4ozYEVVy7vT41BZwoiy8WN29bD/CwcGYbmGPza22AO+B93QaEEa1pVDtbZeag7Shhdn4L/dDXk69Aahfa4yu361r3woV9U1YcAtRp8/ptqPx95H1zfD6sykApBzJ9N8vHxeYmRCDzES7W7RiHE+bM/n5ZSfvr8DYQQOvAcMICabRoEclJKZ3mTCeBHroAdwDiAlNIRQuSBzPL6p8/b7fljxn9q/eXLYy50jAsihGgC/jOwAfhx8zop5Q0XG+cLrDcwBfIc4QB1ygxSIUE3a2iniQwAeXuErtFBvMgMTnMbWs3Dei5MR/ckTqtBpRqlaCaoLTmMPmgQXX2Car6HulNGSECHZNuSCkeaRfKpNPWiiVsKgAc1K8SeTJbrr57mF4iysGjywEYYGYRoTLWfuX4rHD6qFsuCroxqI2PVVFWd6cJAN0Q0VWnXG1GLlOq5WP57IIQSSBQEE5UAaHUqhxIMLazB3GhRSCSgG9yIR0DoGIuwswLp7aAtJ+JnbWgKwFRBzUS9dQvsH4PxHCRiSshFzvOp0jTYuB2u6oBrVqn2Ot9+EMQ0tBowcRKMHuWjlYrAbB4WyrAq/ZN9FCtwbBgWC5CvqSVmwvyS6lnouDA2CY0N0HveOB8fH5+XEp0ICba/VLtbkFJeNN4opXSBy4QQKeDbwPqVNlv+uZLykxdZv1Iy2cW2vxRfAb4B3Ab8JvCrqKYdF8UXWG9gHGyqFJhhgQUgyhLjxFhNiUbiJH74FOFj+zEjRYK7K2w+copQ3qI+b/Dczi2caNjM9FILE0sN2O3dLOZWMz4vWZM/RltTO8PFEMFwHS+qEW6vUMpFkecSCOGilT3cGZ0HSNCyeYImivyvLzbwwHGw50BWQCZAxCRNccHVay20lhLHCxXs9QZyPkmk1WCDo9Mf9Ti05PF3ZYf3RIM0azpCKIuD+49BJAjVOphx+O4+A6/qQTIAvZLKSBRmo+p7y+0Q7ZZs74I2DT7cLbENeHhBMOpBgwnvbYZySVkumLrKxZoqwMYWmC2osF/qvJwpx4OBNiWuAMYnlU3E4WeUG8WeVbChB4pVFUp84CwkfwhX9agk/6/9QIm4cy48cQ9odSXG3r8Zdm9TonNN/6tx9/j4+LyZcKmQ49ArflwpZU4I8TBwBZASQhjLM0ydwNTyZhNAFzAhhDCAJLB03vofcf6YldYvXOQYFyMjpfysEOLjUspHgEeEEI9catDLJrAuUiWwF/grlMIsAR+SUp57uc7jzcgkDsep00OMDGlmmCVKmhoWMSQGOnVGCM4+Qz1pkRzPknpiCbvTwFxl41kGDYdKlNrCJJ08rggQiSxSTUeZrg9QGksxH6lCzMDN6pgNgCmJxIvk640EUjV0XWLlA9iaxqPDgi+dNqiuttDWgnbWQz6j4doGxF1q23Ps35PlWUfgjul4uk3NDmAX4GkrxENDJtFtNlNulScqdf4xmiAodK5bC01xGM9CQxT++ghMPWJBmwFpDd7hwv06TANVwLIJxiwCIo4MePzZnEOhLFivabyzTee2DpUblY/DlnY4MqWqBuNBuGEVFGvwj89C2YKgAYW6El795/UNXNsHog5hDUwPogZ0ZiAX8PiHMx5VW/Dn9+pc3g8zeTh0EGQYSII5D02NUAjD0QnY3gO/cuurcw+9WFwX/vVBWFqCt90MaX/WzcfndYEKEb4yCZfLITd7WVyFgRtRyecPAe9BVfn9KnD38pB7lp8/tfz7B6WUUghxD/BVIcTfoJLcVwPPomaqVi9XDE6iEuHvXB5zoWNcjB8Vj08LIW5DibLOSw16OWewLlQl8H+Ad0opTwohPgb8CfChl/E83lTU8PgGRSRS1Z9yNUEMhsli0sZWVpEiwjwPM3VDL5HHJwlEYXFtipbWeYKLZTzNoSkzwZ7i45wbWUOVBCeat9BQXqQ14uAtGFQzMSKrK+QX4kQ6S7h5jWCmTjBdRVoa0pTonQ5G3UUsarjjAq3u0HTlLNF353EvC2ANRfDaILSqhpPXIS0IdNZxqzrau/JUHori1qqIdge3zyDswagrmcWhGzWLtbFDLQULjn3bQhMW3pChJn0TEv3DJdxpE8o6NOksTUZ4eNYj3SgxAxptcdBDkn0LcHsH3FuEpyoQaYE72iGNypOKBFSi+e9cDQcn1UzWmiaYL8NfP6Zmu/augr1XQL0OX/4m9HfChz8EXzssuavikC0I6iEJNcH9lkTEBXKdBsOAoSoLl0qwqVmFSk9PqTDo68ncc2wcHn5MCdVUEm5766t9Rj4+Pi8EnTBJXmDVzc9PG/CF5TwsDfgnKeW9QogTwNeFEH8GHAQ+u7z9Z4EvLSexL6EEE1LK40KIfwJOoOqffns59IgQ4neA76NsGv5RSnl8eV//+QLHuBh/JoRIAv8R+BSqNv73LjXoZRNYF6kSkMsnB2qa74VMz/m8QDzARhJDo46HRoht7OUyJNp5YWmPJipdGoV3N9F4V4V4U5nKU0UeHWqiacglkSsTS5ym+KF1PJ6+nOhSDc9O4AQEj++7EnfEIP6BHF6rgaaBsMBzILlhkfypDNLSCIkK13d8j7ckn2TiXe0Mhvs45w4QFVU6dk6T2xSnXEuwVEmTExmcmo5X1jBCFqJJkvhgDq+oYc2aeEGNes7lisY8SdkPIshIBc5VVH7WZQlIJCx1d9WBAnDEwJAe7qKAlAf7HEgZmK0Gnqchmz0WiyA1QUMUFl14qgpdJsy7MGLCrp/qld4cg1uWDUx/cA5+OKic3R0PvnoYPrID3rFXLaB6GOpZyB7UqOdR6ZRtKhVATgGLnvJzOA5MQL0VOgZAeMpm4/UkrgAa0kpYlcrQc5Gm1j4+Pq8tXKpkOfyKHEtKeQTYtsL6IVZooiylrAHvvcC+/hz48xXW3wfc90KPcQmekVLmgTxw/Qsd9LLmYP10lYCU8hkhxK8B9wkhqqj/Bq+4wNjfAH4DoLvb/0v9Qomg8U6i7KPOTkK0LV9i8VO5fRluZASbiuFSyJ2h/PUFlmYD9NsWrfYC7sYIHYUS0c98jt7eB7lvy68wMbCdhf0NWGtj/MLHv0DH2glmaq0cndpBtL1CU2CGSGeReb2FyHyernMn6d+3j/7WMfa0S+7vvJnrIg+TaCkSpkokWOJu9x0ci+1gYrYPr64TNMpEg476TuOAY+mk4zl+oXYXW7UcNzphkvkgJ5238cWF3QR1Qd2Dw0X4jRss9p0MwZxUUygNUH8yAXYFXAEzBrRDaZtHU8agzdNJBlVF34QOf38G6nFYEqptYvoS4ubZcehKquR3gGoQDs+omS1QjZ6/eD+ULYEY1yCF+i7Vg7K4awVGURPYs0AB9EWY2ax6IH7wupfopngFSSbh47+lZvH88KCPz+uLVypE+DrkSSHEMCrR/S4pZfaFDHpZBdZPVwkIITahptXetiy2/gD4G+DXVhj7aeDTADt37nwhWf4+y6wjyDouXstvECQjd+OUDhHujaLNTDHpxph+fIFoA2QqFUS7Scq1Cdw9yI57/grv5vfytPdJrvvd73Fi/Spul99lT/QZhtb08anyv8c1DDK5GXrsk/QtHWXVmUGalxbIn6iz4Q6L3n+Z5NTOVRxr24CDiSbggw1f5X/Ot3LS3ITUBU41CJQRhoeUIHTJxsBxOmozZBIGo4ZGVRo8OD9FRh8jEeoBYLQCH2hJE98+Q76QgZwSU3RLNTu0GIDOZZGTFWT3wwc6INGoQnPNIViyIFyAzjg06XDNeb0Bp7GQQPt5Dah1bblycRnXA+M8UVapgWVDIgRhNIJBqA8DLR6UARe0LoE3A6yDSEFZUly1Bn7tVlgoQKECiZe4R+FYHh4bg/duUK7yLzWRiFp8fHxeP+hESLPl1T6N1yRSytVCiN2o0OQnfhTKlFJ++WLjXpEqwvOqBG4Ftkopn1n+1TeA770S5+DzfB4oPMvj4gSNhGjb8352p7/C6CfHuHoVmFdEOHdVH8ItkDtZo6GeZ3Ffhc77HqBlx0HcAbiyup+3Rn8ImmQ7h/lk9Pf443N/yOVDdxMpFjCfmCAwXyO2t51KrZn87FnMOQ8zYqHJH9kqaASwaNFmcFwTNAjGKgjNBVvHKoXIhObpLk9QMNNYIofphZjRytREBJxzILtAaCCghmBXLsODW2y8aQmaARWgJ6Rmi8YEzEsCeKzKaNjzMGwqg9Osq3y2ihW4M/F8oVTG5RssIJF8lBYSyx+bvavgW8eUN5XtqTDh5efVrTQkYKATjg9DOgzFaShGwbI0MiZYEqxZCLZAqQTBHti5Dm6/A0wDvvk43LoDLl/LS4onlf2D9L+2+Pj4LONQZYFjr/ZpvGaRUj4LPCuE+O+oiaEvAK+OwLpIlUBSCLFGSnkGZWN/8uU6B5+VqZaLHBh8HCsGs8KjPRMks/aPWHf1o8QPfpEDl/eQv3uKc09YlAbLeIbAiYUR17dzxc1PYYlOEoEqeZEkIYt40iOAzZrCEQINBubhRWbvrhBMeqSunCU7voS2WQMkfU+Nsrg6TT0SooUZZFmj9SuneEfbd6hfcz0dDTpnnRhH5gUSj6Brk7TyFFMpvj29nVC5kes7DnBlaoZ7Znqp1F1qnkZ7CPYtwpVXm0x+TeNkXigVVwLCQqVUNgK2ZFODzt5uaEnB4Qk40gBRHcIS9oZh3zTEA7ChUZWiBIVGL0E8JDoWdWwChNjZKQgZcGhaVRXu6Yb283K2NA3uvBkeOwo9nfCVu2ByGNY1wgfeCcOD8N0R6N4C+Tw0boSBFvjGKWUF8Ut7oa/xpb/+vSnofcVyWX18fF4vyFeoF+HrDSFEAng3agZrFcq365J5XC/nDNaFqgR+HfhnIYQHZIGPvIzn4LMCslKj83ARZ08DxqzDZdG3c/Kr91EfDrKUNyndm2XowSrSEYQCBtmtXVidMQKeRUv7GLNDYYZ7OnkidBV75JPERZGjc72knz5J7t4zLO7P4lWgZ5XyucpoNfY9mqb/5gjpySy9D56m3B5CjJaJHJJcEbyNzOc/h/bd7xC++S30vOMWurojHFwSpBajNLbDULWDTGYBy3B4cmYrf9F/lNa2GqfFtUQN2JmC//8MZMKCHZcZzB70yM4vO8g5wLIVAnWNg6s9Qr02txcCVGyNTVFYqMNMDe6bgGcFpGKgt0IiBY264D3pNPXAEQ4zggSa6GCA7Wxq1dnUuvL7XKnDVx+FoVmVEhZsgEYTLuuG6TpsXwfXrIEfTsGBssoFszxY3wAzJXhmFla3rLxvT8IT49CThO7ky3GX+Pj4vJnQCdPA5lf7NF6rHAa+A/w3KeVTL3TQy1lFeKEqgW+j1J/Pq0S4sZE9HXvo++f9dO7ZQ8qOMjk3h2dGcNrWIAMTxDI1OvaAYYKeH6ZyBp7bvhl+cJLar2bIzejc49zOycBaetxz9J4epOmz+1kccdE9WNcCa3fDPC52UTJyXOfpa65n275nKXzqBM0DyoJg4RTEmj5HT89u8tkK+rlROr96N3t/67dY3Rmn2p7jmXoDM0WdkBEnaAaJEcLwZulPvYfGoOQpt85f1B1GGjTKsybTOZ2NbRoHS1Ca8JTA8oBlw065BPsbDKxQFduMIi0PqblM1gSVYR0tL4hvhbYQ7A3AD4bgW7U6N/TVeNeGNIYOc4yRoIlWei/4Pt+7H8YWoLdZPb/tGtXypx4EuwihBNzcCn0h+NsFGFqCchHyIWiOwqkFFcYT4t/uu2zB98/BjvZXX2C5UgnUTOD5oVUfH5/XDw415jl+6Q3fnPQvOyOsiBDiU1LKf//T630n9zchQgj6brqJvptuAkB6Ht033YRdWSD1liiBT32e9tuKBKMSoyapRATlvhZWn8hyX2M/51p3kZA5ts+/YGYAACAASURBVAeP0KItMFAeQ24OcMuHA4jnqpgWJLrBXgNGHWoFiWPXqVkR0pVZJrOQexp0A+IaVGazJNZpxGyTaLKN0akZvr84zMGGNDv0CMngTtY2nuJM0SBSivP+1n0QeTtzgT4+UT7Go0tJilIHTyPUIDD3wPWuw5mZDNUJB4mGFwqBo3K1qAusKcnEFoft2+rctwBu2MPVHdxVkspwnLkZQX0B7hmEUz+E6nyAp9M7eeDyMf6f33mQoIhTpXjB9zhfhiMj0HleiC+VUMtMFjbV4dZl4dUUgVgA1ofh8GkVRty1FdKxlcUVKPPT/3CFGvez4rqS4WEHx5F0dxtEIj+fKvr8WXh6EW7sgvdd0nrPx8fntYrnhwhX5GLiapmrVlrpCywfhKbRe+ONuIyT5QBmbwnGBflglOqV3cyu20C2HsOZMHEmolTsIDPWRoJBj/LEOLnJNNs2HOTQrTvpjU/QXR/HTTpYZcgddhkcMZnevpFbjzzEenOR8RjMLanmzkIAmku1fpJRrYVFY5KwzDIZWOQMFfZ5kh0iwlXRtxEcMmkNQmfrW7F4lG/XP88TldtZcnRqJ0PgQq2jTjBm8+gXQ4RmSoRcB8uJ4I3bENOh21BWCUXI2jqPZj1sKdEXQMu5RKoV3HidSC3IxGSMibwGZ4GIwJ0RHDzUwaFzMToHhugVmzjLAzjUGOBGTH7SQ6dQVa9NW0EgxcIwfV6RbyoEu9rhq0NguVB1YLIAv7v14tet8UVU6nme5JvfLHP4sIWmQWOjzq//epxY7MX9YS3U4NsHYLYKbQFegLexj4/PaxGDMI1serVP4w2FL7B8foxGmllOIW5vo+ikWUqmqZBAaBrNlJhrbaLrMoOjjsmslSGSX409EUJ71kBEXT7t/TuCl5X54NRXuOnMw0jXZaQlgbanyq8mDhI9WkYbgat3wDPHoVwWCFMS7YeR0QrZjR5MnWPmxtUUQ2PUZR9FYuyXElPOcHx2A2miXLNuBEc+xOP2HoqOSfnJOFqji0h62MUA3iGJPQ0b+h2mnTR2XlNJS0MupDTlQZWxqZ8M4mQs3HkTqjroQUoiSjBbRkt5pK6cJzfdDN0ot/WysmJ4pNRPutpN0AzSZ87h4WJRep7AiofVIT35b0VWuQZdP5W8/vY10BCAbzwNySh86CpYc942cwvwjXugWILb9sLWjS/uGs/Ouhw5YtHbayCEYHjY5sQJi927Q5cevAKGBjsbYLQE7+m69PYvFs+DUhUiITB8qx4fn5ccmypznHi1T+MNhS+wfH5MHckgQUqx1SyINFXi1GWQqhukLsIEqKMHqmw0TjId6EYWPEr1JAlKnHA3shBPE26T/GnwT/jzlj8mOlZjqd7Ezc338zdN/4lkZhrR5xI4orE9oTFWdKhnHZzNMY7tfgehjhD1xiTuqhQhmUfIk7ilLuZirVR1wbXXTHGlXA0INDSkFqJeCeC5AiPhIksaRME6FCTUVsfNxugLw7B0sbLassu7TahPoxYG4dm4oybMGrAP1TzQ0qiHEqBBNeFBh4QAEANsSHW45MfamX0a/nd7ik/feAPpqEOu2MisA/3L5pqpKGzoVO1uOjM/eY9tRyW/X776+e+9rsG1/WoB1cuvWIR4XD2/6z4oFCEWhW/dC6t61eOfFSGeH3YU4udzi48E4A/fombezm+C/VJi2fClB2B4GppT8OFbIe77bPn4vMQIP0T44lkxmcMXWD4/Zph92IQoiSA2YWxp0ulN0l0bQzo6U3orc2aGemCKX4p9mbbALLNWKw3xPAcaLyOYqVCspwlIj+b5HOWGMH0DZyhFIzwrLmP7jEYgbiM3C/Saw7apBWQqzFfW/gLFph1MyRglLY5AsqF2koy3wIIZwHWbcLUIlUiOMBZCdqOzl+1mkQfDDiIIICDogQGm7rBx9RJb+oeojKcwn1nFfIuHW/AYWGMy1iOZGtQxIlXsfAAeBopAVIOIBwVPdXlu05TDugci6bHhLRWKTzns/3+biIQgkdH5nhblg7cIPv0c1B342C6VdH7yLLQYkE3C6Lya6XGXG0W9Yxd0N134Opw7B5//PIRC8Nu/rRzRK1UIBiFgLvtYOS/uGjc362zZEuDQIRUibGrSWb/+RSRynUckAOfrnZqU2ED8QglkPyPD03BuEvra1OPjI3DFhpdk1z4+PssYhGjG/2BdDCFEVEpZXuFXn1xpe19g+QDgYLHILB5RJrU4rivY5T7HmuwQ35h7L8/wf9l77+i4zvPO//PeMhUzg947CTaxd0oiKapZkiVRsiVZjizb8sZxi+2ss06cs/ltEm+crDeb7KadxClOYluWXFStXqnC3kmxAQRB9N6mz5177/v744VCUARJkJJMSprPOXOAuTN38MwAA3zxlO+zkgZPO8HgOPmRUTKahxLPGAuLW/AHwE57eVmsx3KDLEkf5Kg2i8WztrOuaAuF5iiurTFeGCCvP0N+chSP12KsqYTN+St4LbSeoEgxJgoQrsQVGm955/Px+NNEA/mYwiEiNARgYeMXHkxxLRs9GZ7OG+TwkiixtgAEJEXBNI1XpSjOH6CAEIVXDHCfWcTWlmKaRwQjTYKSoEvUL7HGBfQKZRbik6C7kAaaBIQEGBKSElwI1yZJnYThAyGChS75rondJ/jVk4Iv3ATFfohnlX+W6yrPq3gCvvmbELdhfwvMqIU5Nap5PWvDE3uguQ+W1cONC05lloaHIZVSIioWUwLrthvUY46OwfrVat/fOxkZg398CO64HuY1nXk7gKYJ7r47yIoVXrJZqK3V8fvfm/9aHSTPWgn+sC9N31GTG2eN8//qCigUoXf1uP6JpQTRiRJt3vuUKcuR46OMTZo+jl7qMC5LhBBXAv+CqmXUCiEWAV+SUn4VQEr571OdNy2BJYSYBfwDUCalnC+EWAjcLqX80/ci+ByXHgcbgc4Y1dgywVKxm9pkN80jcxhtL+BfNn+Gk3szRBaAtb6UzetvJuYUUH9kiPK3erjL/zrbF6/m2bL19EcrWTvvZW6peB6hCRIE6NSqqYn34B9KcCB/LluC6xCuiyX8hKIJpF+lpw1howkXVwqQoOkm5cKgZKCH8XgPhhTQtByAGcLL/Xo+vyiPIioTeISgyPXi0cJkB9pJJLP4AmAmoX7UJdCoc7xD4qQEwUoXOQJWrwAL1ZdlAPN18AFRCb1AXEDEJXHUJHPAxDfDRQiJ0w26IXASYKVV5sqVYE70B916PQwOQ1U5vPgSbH0OVnxNiSuAt7pg5wmoKYJNR6GpHBonpgqXLFECKxCAmom+pqZG+M5vKwf2s5UGDR1KC8E/RTuVK2E0CwWmElmNjeZ784MziZ1ymO9mhrF744QtP48/VM/a/9rGF30X2TA2QW0Z3LkW9rbA0lkwr+49CjhHjhz/iUTkdhGenf8LfAx4EkBKuV8Ise58J003g/XPwLeBH0w8+AEhxE+BnMD6kGDiw4MfHJdrkm9geXXyrBSDiSK+9dL3ePVXGYrmQ2tBLXuttcx7eB9LMsN4bPCOW9QcOcnjz3+Ob9z3p7wUWcctlc9jC51BShiimHTaiy+bZl3B6yzfvw2jwOEp7S6cRg2vN4vMuhQaw4xSiO66NGVaGA0U49FCXJ10yLTupdg1Ce59BCIVUFoFwCeCA1SlWtiWKGMsO4+AY7K6QlBXtYCtnR30HSpDGylixkyDRI9GyHKwD8J1EZdNmp9EFJUW6RPg1yCDylr1uJByIQTEwM4zsOcKXI+OmKeDH/LbYaQP/u0ncPedUFGuepFsCauXnXptFy4Ay4LKylPHHFdlrAxNeV25k4aAPR645pozv0eDMchkof4sjd5+H9x769QC7OlBeHMM1uXDx0sv4gdkGjhIwkaMaMqDJ5jGE0zhyKnLhIej8OIg5BmwsRyKz706k5Vz1CVHjhzvDyY+ysi9yc6GlLJTnN724JzvnOkKrICUcsc7Hvwiu0ByXI5oaNQyn0ziMfxilD67mMBoig2JTfjqEiz4PIwMQH9JLYsffAqz2M/JkSzVToZQAQyXGPi64Pef/Gvyfj+BMAQDlHFUzqbdbiAoErTVzuJXzkY+4/4Htx16huViH/tTi2mpnkEoFee4bxZxI4QUGv4AoFWyWFRTYFuMeQSrByZqQ5k0AGkG6RcvMjMQoNrXStgpoEivw6cBlDIrUMpP26DFq6YW59dJCvI1uqRkYb9J75DGeNYlYU2UBF2gC6gBlmjg1WBEwrBUmSzAigqIQnqGoCcfFvXBeBSeeg6++Hn4URv0pODbc8E/8e6qqTmViXqbBTVwpAeO9cGqmdB4jp4sgO3H4PFt6vO5NXD/htOb1UfG4J8fhmgcPrYW1q06/fyRLGRcGM5eyE/FhbFKFPFl0+GhRV10tvu46dYxPuE707dhLAs/7YZCE3rS8HA3/Hbj+xdXjhw5zk+WNL0cu9RhXK50TpQJpRDCA3yDaaz5m67AGhJCzGBi84gQ4i5UASXHh4hS6jG1Kxl1N1PUfpSCkREM18GtgFgXxEJwfe8OOqvyKDkwRFUxGLcGiHvzeCFyLYlAHk63SZ19gqTlpd8opc2eQZ6dYChVSVaa6MLmr+t+j5FYCRva3qS6rZNIOkNn6XyuoJv2UDV2SR3oIUIinzz8xCIaa+w6Sk4ehHnLobIeAIcUACYRbC1JUIszuTrW0gFH26CxCrq6YG+zYO1CHS0OgVoIJKF2jsbxEYdsBjUHYqOUSwzIShi1wTIx5qbRCiVun459xIsnX1C0ADK66rUKT7QZVfpVf/zbjuauC0ebYXgE6muhZkJveE24/+rTndpdF954A3p6YO1aqJ6kTXa0QElYZa52NcNtK6BgUmvT8XYYi0FVGbyy9UyB9YkyWByGxvexf8lA4y6jnI/ll5GOuBQJgSbO7O9KOuoXSZ6hNOyg9f7FlCNHjukhyRmNnoMvoxrZq1D/hr8AfO18J01XYH0N+CdgjhCiG+UK9JmLizPH5YpAEPE1EUhU4460MrDPIToA5bOhrE6Ny+sjFjPCceSgTf+aPEqCGh1NNSzR9mLFdGwngH+rRWxPCF+hQ//scjoijThCw6NnQIJXS/NWyWKuHXiFlqI6xgbDzO6N4eQFCJgm1tIqvMUV5BOijAjVWiG+NStg9T2npW18lOGjlBR9mIQJcnpzztt3lRJqa6C+AgryYaQe/tmFwUqoN6Bxkc6xQ67qw/JzaiSuT8KYiffaFMZsC9cSmLOy4Id4v5/ri0GXMK8KbtugTrml6vTX9OVN8NKr4PGCY8MDn4GmmWfGCHDoEDz9NITD0NoK3/kOGBPv0Bnl8NR2eKsZfAb85Dl44OPQOaweIxJWU4xHO2DRFINAIQMWvbte82kTEoKQOHsvR6kHGgPQOjGLc9P7VLLMkSPH9DHxUcHsSx3GZYmUcgi470LPm5bAklKeAK4XQgQBTUp59h0hOT6wSOkg3ZfRjQ2MHt9MyyaHqvmQGIbwXKhsAmsA7HGL17qg3mNgSo21g9tJ7tMYt0KYVgaftMh4/JQmulic3s+WeSvYU7OMXruCEbsEU6aJNPbz3NybMJwMejJDpi9Bw1iAFduzFNc3oRdP8UZ/x9i/jpcqbsMmjkEQjdMbt2fVwuJZcLAVivOhYDWMu/DjN6HPBdejJvyqC6BqlSDlkyRsQUYD0hL6NQhL9MYszqgOrsBJg9lkYfT7qTFBL5PUFb3dlH56fFLCG1ugrlYJpaFh2LnndIE1mWxWPcVAAKJRdf7b3LgE9h+FTDnMb4C3WuD7D0NGQCoDoyNq2u7oGHjS0DoAlWFIpqEwcvaVO5cCQ4PP1kBnCjwaVOemAnPkuORkydBNy6UO47JECPG/UT3nKeA5YBHwO1LKn5zrvHMKLCHEt85yHAAp5V9dTLA5LlccwEHzlpL1zaWsaR+RchgbBkfX0fwebFKcOAwjAgr2pelOp+lrgcJKFzc1ju6DrkHQzQSR7lYqw6149BTRogjh0DhD6WGGOorZNr4OX0mS+itO0ph3lEyxSawOemvGucmX5DwtSf+JhoGH/ClvMwy49ya4IwOmAX95El5ph94x0KpBaJAohqEyMHySqjkWlTGd9kMG42OCXqR6O2Xh7eEaYUjkmIbtAatD4u13ed6CLSH45jc1BgYELS1QXAxz5igX99ExKCmGVPpUKXEq5s+HEydUOfOee8CcpBcNHZbPUo7uO/apTJZZAB9bA1YKOgdhwzI4MAIDY9DWAz97TLmfL78CPnHDNF/QXxOmBo0XYZSaI0eO949cifCs3Cil/D0hxJ2oEuHdwKvAxQss1AwVwGxgBRMjisBtwOsXH2uOyxEhPAhtDtI9RvE1H0fLNjM2kCS/wcAfysfylmN1DdNzfJBQlU3z1jRiK6y/TlkVyCxEx9REXCwG6RTgwPy9b9FdV8lbV8ylKtTFwdRC9h9YRsITITMaILXWz6y8t0g6OpGyVl4xHmEjDfjOIpymS9+omtarmnBS/0oddJ+AV8ZVn5Q7DFkvWCVQKUHXdfxCoOuCvDwwigV2D2Re8eO7IQUayIxGZrMfbzs8dwDKSuCOJfDss/D888qBPRBQ037llTCzCQ4cUl5Wq1fAuilXgio8HrjrrrPfvnAm/PuzcLAdNK9qom/uVkKxMAilQWiMwNomkEkl6GrLYfchuOlqCHzAM0UDCRjLQHkQwueZOsyRI8eFYeKjklmXOozLlbf/3b0FeEhKOSKmURY4p8CSUv4JgBDiBWDp26VBIcQfA794N9HmuDzR9E/i8iQe32uUr1lGuRVCd6OgBzBEFY5sx1cZJmW1QtbGkwceH6TTkInCoQMw2q8atnUfzJ4H11dK5h06SHNBNVq5l6VX7Sa/aYiDby0h1h0hE/OQCEZws4JBUUTAOcBe7SXWaOdQG9PgtbcgbcHnrlPXQwbcNgf+fR8k+8DZDQiQXvCVaMyvEEQkFJTBjjYobFBLqZ1DPpInPYgCFzmuIWMaqa0OzWk4HpLseFagZQVuQGI5koCQBL2Csahg3TpBw0wY7AP3vEO956ZvBOrKYfFM2LIbYhn4yq0wEIWfvwa/3Ay3roQvbIATnTA4Bt1DEMqHf34TKiJgRaG1G2ZVK28p37szcX9PsVx4chCOJmF2ADaWqBIiwNZu+I+DoDsSPQOfmitYMVM52+fIkePdY5Ghi9ZLHcblyq+EEEdRNY2vCiFKULbU52S6Te61KDvGt7GA+guNMMfljxABdONe8KwA/YdQcKpx3AAqrmwkOvArxjr9SMPE9I7gOmoAr6MdBvtV5kQA0oKD+9X03PieHvL1TvTF+YzMKqWipJvKa7t4s28dad2PJT3oXpeUDOATGVqdnzNHXEOBKD4jxiwWCeJEyEecI6W9cdXp/lIA11bDH94A/7QdhrohP6EWMwsJxScEbRoMJqHYB+Um7JwP0R6QwxpyWINGYJMLSQFI3LhGRgdKgJgLtiSBRioJ/kLJkychmRVYcSjxu9S/JLjvExfXEBXwqoxcwA8rFkPQD1cvgP/1C1i3UPVsjaeUS3w0BcILCQsOd0KoDnYeh0wMrp0NB05AaQFct/SiQnlf2D4Ou2NQ7VUfSz2wvgC6ovDd1yUndsfpOKzjWibPlmp86Tadb9x1usiyHdh2GDoGoL4cVs0BPeedmCPHtHCnXqn3kUdK+R0hxPeBqJTSEUIkgI3nO2+6AuvHwA4hxGOoac47gR9ddLQ5Ln+8taAHwEmArpplpOPiGXcpravC0QYJNhQhaupI+g8R0iwScf5z5eXbuiadgmeegvxy4HvbCP5wJcOUEnTS+IwEq8s386Z7DQOyjJnmCXzZFEYsyfAReLTwz7n/ij/BQ95poR1gF6MMM5eFVL1jcnAyU2VnNAHfXgxfvQIS96lSU38P/N2P4ZUnIFwIdqVyTu88DmIfBL0gCsGZDW4fZE5OPDt94plGxCm3UEOAA64pSUQE23+VhSyE7xwnujLFH7V5CWwq4Y71Gsk07DwC4SAsmTV1I3oiCc++oqwgPnYN3LgS3tgPhWG49zpVisxmITixZHo4Buks7D8OjdUw1gld7fDyIYgFQffATEeVIzcPQyAFK3wq7EtN1FG2DaYGAQ3GJ5z2XuuAWH+arpMOVsaPdKCzV/LQCy53rNWYOcnO4oVd8NoBZWGx/4QaALicRGSOHJcrJl6qOcsEzkccIcRnJ30++aZz6qDpThF+TwjxLLB24tADUsq9Fxpkjg8QmglFd8Hgj8CJgVFIdutRMi/uJBQoZOUXf59mdy/JygSdLVkWJlrxhlL4x5RruTOpHJZMQboDvHlxAkEfjtSpzHRz0qynVA4S0YfJZ5S0o+G60LcNYo910jXPpOuKl2jkjtNCMzAAgTaNtQ6DgxAMqr6oyQRNsDLwD8/AG5vh1R1q4s7bAUtGYMVK6MtAeYHKBkU7odIHRSk45BMkYlLNBAgBEVTiWKIEpgfwu3A0AdkseqFGoHqczBYfmQaXv30mS57u5WCrzZETLv48ncA9OnPqz4z/lTdhz0EIBuCnj8Hvfhmum+QSLyUsbIR9E5n9RQ1qHU9jJTx1Etr7wfTD/hHIpqDMCy97oURTnlw/jzu4aFzlv/QKa0kIdkWhI60E37KwOm4I8OouwtCQNhheiSZhNHqmKN3XCjWl4DHA71HXcwIrR47zY2HRQdulDuNyZcWkz33AdcAe3guBJYSoBYaAxyYfk1J2XHicOT4wBGZD+dcgthlSLcisDd5GhK+W2bdvJB4K4En5cZbdzNFHv0+g6BD+MRdfnkZ/j4MzkYGQUnlAea8soL++GjOWxtE9mFaaTq2KOcYRbNvEp7vEPPkMzr8KceQIckkVPemnqfRdhW/SXOF8lpEhTYDzj6G98grU18OqVWfe1jEIR7vgzWOACWEfZPLU9OHcCuhuggUzoKVb9U99+V6YVQHf/iONndscBrsEegCyMwVWM4BQWSy/BiNJyKaBAG5cx456ySYCOFt1dvcbfH2zRcfxJKTiVNQa3Lm2mDn1Z74ds7aaIPSYKlP1ToSAu6+G5U3qdW4oU8fWL1EZvENROJ5RWjBoQ9IHfVmQBliHHQLruzksQlxFwTR+IN5fKr3wzRoYyEKJqfYmAmyoh+fqPRzaHSXrd5BJHdcQrFuuyoCTKYmo4YayAhhLQEP5O79Kjhw5zoaTmyKcEinl1ydfF0JEUJW9czLdEuHTnKr6+IEG4Bjw7ra45rj88VaC924APJ+0EE0H0AoK0EtK8WLi9TVjYOF+7i5Grevw/Px5HCtDfeUg4aIoh1+FTBLMdYX4H7qGSucEcSMPZ9ggXpJPv1uDYVvU+HoQE67mohL6vnE9PitNLycY5TgVkwSWjj4tcQVw++2qHDYVTZXQVAVFEYg7qqE968LJcaivAbFDleJWhSCehOVzIOCDZfNh4WwdgTo+1ACPPi8Y3yQhiXIftRxUOstACoPhh0rQQgIZ1wiEdNqHkziOwKNrjPVlOf5WkvWrQmja6SmZa69SNg/RONx925nPQUrQNJhRcfpxXYfCEpjdCD1RcLKQ8YCTAWFBfhDC44KStggblk2/092d2KH4fvlq5ZvqMpnSIHz3aoNIIMS+wxZuQnLvMpMvXaud0V9159Xw45dUD1ZpPty2+v2JM0eODxsevNSS21k1TZJA0/nuNN0S4YLJ14UQS4EvXVxcOT6oCI8Hz/LlAEhsqullhGNk0SkLmiz6nW+Q/sTv0P7GGyQP/i3e7A5CVwTpXrOc7IICjHgWmYZUxM/i5gM8XXILtq2zKPgWBg5CuJBx0F/tpDy/n8SKWaQpZJQEFeeJ7Wz4z2FNEPDC/ddAWy88/pRyqtd06M3AEy/CA5+EJ15WMunum6G4UJ33m5+HV14D24Z1V4O3APLqYPhmnebXJd3HYDwlSI6AymqBjOs4aQ0k5JWCGbYY6rfwaA5CaPzyCYcDR1P81he8LLjilGrIj8B/+Y0zY7dtePJl2HMYGmvg3o+r5nfXhe4RsGw4PgAFQbj7SvjVNmVMH5dQAYRcmOnVuCMcoTwFr76lSqQLZ0HNWV7s3j744U+VG/4XfgN8vqnv916TzMCDrwsCKQ/rKky+fgOURKZWeMUR+OadanrU7728DFZz5LicsbBo5+SlDuOyRAjxK04lmXRgLvDz85033QzWaUgp9wghVpz/njk+bLhkEWi4jGGSopINuLjAOElaOFb7MVru+zgF1rXMe+iPyfzon5jftwm3qpET9XMZN/Mpb+1Ck5K0DJAvRnAnHhkEEgEZB1/HEEZFGCIm2Xw/tpZGw5xW39WFUBSCm1bBs1vAE4NiHaIZONENnQMwNLGzoGsQGmvV56WlcO/dpx5jVzuYMbANWPNJQWY7JGf7efBHFlJmIatDTILuEirT2Hg9fOXeEF//+jADAxr4A2T9AQ53wrf/1Oahf9QpiJw77qMnYPt+aKiG1nbYvBs2rIGfvQmHOlVWaygBIynQLWgogBUlMGjDhvlAHGaUwMJa+NdfQv8IeA3Yug++dM/UIquzZyKbFlMfK35N5bfxJMTSUF8CJ4cEIwlVCgQlKOMJ8PtOGbNqmso05siRY/pIciXCc/B/Jn1uA+1Syq7znTTdHqzJju4asBQYvKDwcnzgGWAfg+zHwE81q1G5HQsdLxZJnqSCXhJEpM7wcBuBwije+28g/fQmrOe7KCjtw6gqx0wHKRBjFMl+LOFFAhouLrp6lx8fRRtMUP/sAWrurOfI+jA7vTsoSp2gpsdhQeFSvGW3gvHu/sK7Lvx0OxzuhdlzYTCqLBny8uDTG+Gp19XyZIAXt8KVi07tBnybaAoe3w8NhdBUDO0j8MlPwKvP+Fh9tWTvrjgZy0XTPFyxxOTT92t8/m4oL/Hw8svlNDdn+eJ3JOVFKp6eQTjWBqsXnzt221YTkZqmerQyFuxqhbc6oL5UZW7sLLyyDyqLAAl9Mfj+Z2HNJC/B6WX0ugAAIABJREFUjh7oH4a6SnW9d1Ct4ZlKYF0xG9asgIIIlP0a9weWhKGpHFr6oCIfaiecO6Ix+PETKma/F+6/A2orf31x5cjxYcKDlzoaLnUYlyVSyteEEGWcanaf1k6h6WawJi/4sFE9WY9MP7wcH3QsYgyyDz/FWMQZ5CiVfJw0zyBxGaCRPkqpkod5MVZNSzIPM5uPVl7M2jtTmCf3MJ7wkpReUtUF5O9pYeXwHvaWLcR1NKShYWAjB6IwmmR+QZqGfIutV9SSl9yHaRvsMWbzDyfX8ZWeZ7mTI1D2u2Bc/F/U/igc64PGEqgvUjv7ajzwuWthdgNs2qV6n0B5Tk3lp5SxVR+UZ+KdpAkorYBvfQM+doOfkyd9BAKSyjrB468Iohn4l1/CVz8NhfmCWbNMSops2rscQkFobDTxeiAeV5OP2ln+oZzdqMREZy+EgrBmCbx+DPJ8k5Zc21BiwJoqFXt8DKpOd7zAY044TLjqa1lZ8J3FJT0YhE9O0Qc2FbathE/QD4XvzpAfQ4fProWxJIT96jrAi5thYBhqK9T36cEn4DtfzpUFc+S4GCwytNF+qcO4LBFC3AP8BbAJlVn4WyHEt6WUvzzXedMVWIellKc5twsh7ibn5v6RQaADAhcHFxsNEw8LMZgJZOjFT5CXGbJj7E1XMZJfzPNzr2NO7CiHZt5NUem1NKZbWJLYRt7gAfrH8tn4zCM4H9PoLq3AEhpoBroIUH90nFmeHvZ9az2LAgfxyjSm67AqsZ3ZlUcp7R2H3Qm44t+h8Q8u+i/q2+LFnWgUry2Hu5fD3AlrrftvhSc3AQI2XjP1lykKwsxSONavxFVRAOqKIOCBNatgzSoBCH72rBIbxQXQ1g3tPTCcgB/+2GXPlhSDCZ2yCo0br4dXX4BHRlSW6HOfhXD4zK/r98Fv3QvjMcgLqEb+4hAkMvznOIBpqt8EZQFwbbVeJ/KOXYjRGIQCap1OaRGUF8PKBe/8aheGbcOPH4fj7eo1+43bYN5520HPjaZB4TvE4dCYEpegPnb0qEnLsw015MiR4+xIBDJXIjwb/x1YIaUcAJhwcn8JeE8E1h9wppia6liODykmAaq4mj5246OIclSzu0aArAxQjANC4kiBJVxckSVV4+WEU4voh/qCZgriozhpH5VuDNefxUrpfO6Rn2Cl8+gva2CsvA73V924kRL0EpeqvAE82Qw+j8VINB83ZbLc2oOfDCSr4cBTYC+F2Tdd1HMqDcGqRth6AqQLhoQCP7R1wcEWZdR5/+3wl4/DfX+vBM9vboANc0Cf+D2kaXDnEnipDYIarK1T4uqdVJbA3sMTWTCpXBz+8Rl45AcphjtSIKBnTOPFJwQrlhVRWyPo6oZXN8HG26eOX9dVdsi2Ycs25T9WXQhtA0rsocMXb4K2VhiJw1XLTxcfO/bDo8+B1wseHeY2QF0dbD4IV8yArR1w9QzoSoPtwqry6WnZvkElruqqVGZp0453L7CmYtFseOwF5d4ejcO8mTlxlSPHxeLFQ/05jJs/4mhvi6sJhuH8avScAksIcTNquWGVEOJvJt0URpUKc3yEyGcG+cw47VhPFn44BlmpszT/Stq0FwmZUTy4+LUYw04B15svcLP7HKLcJS8/TjruIS/h4B3xko1HMLM+ao54CDeXkO6MU3DLLQjtODHnVfAIMv0eRkoKGcmP8LT3Y3jGstxxvIVlZKH9DahdDf5JdSgpp6UEhIDbFsGyOnhks2pq/9snQI5DYQhe3g4v7YEjYyCDQBbe3OXyZ5+VfHbDqXrhz07Cibjam7dU8g7fecWaxUoIdPTCjVdCfxpMAdm0avHXhCrpjY45CM0FdLweGByC51+Gzm6oLIcVS6HkHduDjh6DR58AJHz6U7BhoXJ4ryiAsnx4bR88vR32nFB+WL9xvTrvjR1QXqKmD8uK1PMt6VT+WTubQRTD7HL4ZZta0TOnEPKnsWQ5OFHaHI/BWOz964tatVj1xDWfVPFfvey8p+TIkeMsSMDNZbDOxnNCiOeBhyaufwp45nwnnS+D1QPsAm4Hdk86HgP+60UEmeNDxp4U2BJ8AsaThXzebxH27eQXehH9ejmLfHu5sXcTYU8M19HJeP30e/1ko1GC5R78rolHc3CqsnjqltC7dRbW4BieQA2eZi/aojHGgyH2Fi/ipdQNNHddgZX08o9hm0/bm/je6E5CicFTAitzABK/AN9VEDh/ZksIqCpQvUfFYWjrAZmGufXwyi5oGQA5E+VtYEB3u83/fcThU1f78U70L/UkodQrObBX8uQJyc1rNKqrTxd4hgEbJsxOW1pSPPLLMU60G1TOCBEdy+I6kmAIlizWcF2Njk7lgL+/Gdp7YW6Tyght3w0P3AdtnWp6cM0y1av1di9VJMxpq2MABsYgz68uhzvgrx5XJpydA9AQUQLLykJWqum70gLoGVIeXI9asKQB6gREppkdKojAfberzFVDDdy8bnrnXShCwPIF6pIjR453R4YsrXRe6jAuS6SU3xZCfBK4CtV58U9SysfOc9q5BZaUcj+wXwjxoJQyl7HKcQYzPLA1BRkJ13kgIiq4Xf6IJ8RteEhznf0yQTtJS6SRNwuuRDctNI+kurGb5fF9+ByBGZ3JWEER+/1HGWiaga+7jKu3pqnTP8VYx7/QPKeMPc4SDp5chp0xqPW18bnkj6iO97Ij43DN8Fb04okaVPYQyCRk9kxLYL3NHavgyR2wYTGkRqGlAxwBwo9KSUnUivNinWGfzg9OwKfroMQHd9ZIvvt3WTq2QKBJo/Ogwze/qVNUdGYWLZNxefDBYfLzdBpDaRbdorOtJERmPEF5lcaM+Xk8cLdgYAD+x/fgzW0QHc9yaI/NjddpzJtn8sMHNTIOFBXAQ4/Df/syfO1LSmBVV5/xJVm7UHl99Y9CLAv5rspujUXhtaPgOaEyTl/4OLR0Qvcg3LwGdtkw7IDIg9VT9IGdizkz1CVHjhwfHHIZrLMjpXyECxzuO1+J8OdSynuAvUII+c7bpZQLLyzEHB825vrgmwY4EipMkHI9nsxBCqwEGa9BxIkxQAmPFN2Ox5/E0YqppYO4G2RXeCG1ohtZOIojsxymiU7Xw0BdBa9Wayw/Nsbd0Xvp9gzRPVaLnfEww9/MxsijLBw9RCwSZDxoMDJ6kBI7CUYA/DeACIA5/4Kex8xK+NbEykMpIZGCwieg61eo7VwS9W5xdRbNhagNP2uH63D4y+/FeP4RH7at0b7PZfFijSVLYMECmDmxO3XHDpstWxxmzBDYtsTnE5QUCGoqQbvKQ3W5h+MdcOgEDI2r7NWOvZBMxkjGYpCJ89jPJFsr/KxaV0pprY+8kNr5eLwL9h5XcV/LmRms8kL49r1q+fEvtkB4Yi9jbRnsOA5L5qhG+YO98Nsb1SSizwv1WTiQgVXnMGvNkSPHhwMvHmZQc6nDuCwRQnwC+D5QispgCUBKKc/5r+f5SoTfnPh467uOMMeHltJJP0VCmGTTt7Fs7CFeK/XRqVdTmBxn3JvHTDFEJ8X4SRDRoyxJ78cYdRgoKEZKGDXz6KEUV0hGzSxbZvoYHZ/LbM9LMA5SgwXhg4y4RSRlgHTWT7U+RAyLEm3CZVIvhuDGC34OrgvDcdWgHvQpwXHXBhgYhaeOQVsGTAeWzIabl0EWOHIEXvxpnNdfNPF4BbruEIsJdu6EF1/U2bZNreqZP1/yxBM2RUWCzZtd1qzJZ/fuMQoKDG6/NcTeo/DEC7DvCKxYCD94VC2i7k9ZZKSETAL1FV36ei1efNph/d21HO7Qqa2BP/sRNFRBQQj+5jH41G1QVQnlBvi0Uy1pPq96nm8zGlelyxlVqmm/Y0iVDosnTDyrTXW5nEkmIZWCwsKcPUOOHO+GNBYtdF/qMC5X/jdwm5TyyIWcdL4SYe/Ep1+VUv7+5NuEEN8Hfv/Ms3J81MnLbqexxyDrOcazkRtZY+7A0CSWMJAIRmUB12deo7hrhKGCAirH++kwa8loXkIiRswMk8Ug6jPYQQCv1cT88CHaRpvISgNL97G1ZBVXjm3DEDYnGpZhaCeppvF0p3c7Dake0DwQqDrrX2DXhZ9vg4Md4PXAF9ZDdRHUlcPv3Qf3DSmfq4ZKeKRbLVAGuLYa/tV20DQDQwfdL4hF1QqZWbMEUsLu3bBowqA0kZAIAStWBLn99jzERDw3XK0EXcAPM+vhF2PQcQAyxRrsTKHE1cRYIFmSyTTFvgRmKMyrByCjw5bjUF4HcQOe/zmUzII8E4r3wkwffP4eKCuBlA92dUBFEEYT0FgGsRSMptR6mcLQlC/RZUlnF/zwP9T05LKlcOftFyeyWoahOKAmSHPk+OgiciXCs9N/oeIKpm/TcANniqmbpziWIwfeQBOz8g5R2NNHNBLnSOMsQDIsi4iIUXTXxedkwJIEMmlMx8En0hjSxtI9IF2ShPCRpsupRloueR6H+uBJTmbqWF/4Or5AhvaKGRy1q1lfUk4bh3FIIWQfUbrId7yUt7TjSSYACUXLoe4OEGf+AhmOw8FO5RA+EIVtx+GuInVbUURd3ubeOuhLq8xQoReqfs/PH47H2bc/j3RcJxB0mTsX0mkYH4eVKyEvT/DAAx727HGYNUtQWXkqBtdVNgsLZsO2vcrLKVQG4VKJeRyy6Kg1Qm8LRx2kh2NHbJwyiasLpAdiJsT7wciHvDDEDoMeheQQ1DRAx1ZYuwKoB2cArqxW4mo0Dt97ChKWWhh9fAhWToiswSGV/So9tWf7smLLNmVVUVMNe/bCtesh/wJNTR0XHj0CK6thQ/37EWWOHB8MvJjMZIomzo8wE6VBgF1CiJ8BjwOZt2+XUj56rvPP14P1FeCrQKMQ4sCkm0LA5ouKOMeHn+BKaptq2XvYQ7j7UapnHGPQLSDpBDFNC49ME0sHCZsxfJkMpm1T5fRR5u+nU6sio/kQQpJ0Atiuzqgngm14uaZ+F60DlRwZW0yJOU7Sp3NFpIyd7eV0xQWHPRZWfgXlgVLmyGPcFOlmqbYATQLDO6FoKYTqzwzXCz5Tiat4Wq1mORuagMpJmY7Vq/z8xV9IHn0sxdioznXX+WhqMtm+XTWOx+OSH/wgy9hYloULDebOPTWKNzAIP3oYxsZh/VXwtftV4/lIwuVPtmbosSQ9BSaM+oDUxFmF6LqPlsMm3phFqNFLSndJuZC1AA3GM4AF3mJJtkkj6hdIHbIxCAlY0gRrGiHsgcM9MK8eGoqVtcMLh2BlA+zaC48+pb7ibR+DNSvf1U/E+0JpCezZB66jJinPtdj7bOgafGUF+C9qK2uOHB8eMmRppvf8d/xoMXl3RRK4cdJ1CVy8wAJ+CjwL/DnwnUnHY1LKkQsIMsdHDK+/nHuWwdZUAz1Jg0ymlVSqjaGCIJa3GtcTob/Mg8AmFI9hxz3MGTjCkco5DOvF2I7BeDqMnnYx/FlKzS78fi+frs3jQDbDKGW0anU8d8RDecpguxYGn0WJ0cuIN0a3LGDQXc28xDgB1wPo4GamjDXghQfWq8xVSRiumjXl3c5ASsnx4zZer8Ef/Y8CDONUferQIWhpgUOHbJ57zsYli8eb5pvf8PLfvhVE0wQvbVL9Q1UV8OqbsHgBlBbDc89kmVcumfsJjX2tHrZsKSY5MEImrgM+amsNyit0Uq5LTzLFuONihw3cPBO8QBxwIdMpYIfEzUgO3KSRKIGwo0xFv38UFuXDAo/KUjkuJC3Im/C52r5bTSlqmvr8chRYa69SGayREVi9UhmmXgzhizwvR44PExKBS66RcTJSygemcz8hxB9IKf/8ncfP14M1DowDn554kFLAB+QJIfKklB0XHnKOjxKmf4w51KCF6ohaLYSMOB7NT9JXSzDWjxQFjPmyjL5Rhrc1Rt811TTXzyVoxBBpid0NhA2SJR6Op7NYecNk9GqEyFKesjke9XIwL0zGNDCkJBxPclVoCx4jw2CwgvY+wdx0n5owDFSdNc7qolNlwenyxhsZnn46OdFX5eWTn1R7W1wXDhyA+nrYs8chnQEMD1bW4sGHLW69xcucOQamKbAdVSIUnNp1ODzsEg4L/H64qVhQHzGpra3k2Wcltu1iGODxC4alw1jUpXzmCdpengvXoKqJLspneHcGYjr8pg4Rl9YOKC3TOGzBnWHYOwqFZbB+Frx5HCI+uGdilemcJnjhVdXTdM1V6jnFEsony7xMGt8NA9ZdfamjyJHjw4EPk1nktqVfJHejElGnMa3EuBDiNuCvgEpgAKgDjgBXvIcB5vgQUkYTPRxBIAh5GqmgnAFeALOGnvQSxscHsEwPndqX6de30z1SR8IOEhsP4RtMkG/HGSouJhIZISN0jlrgD/UxO1vJ/N5/ozN1MwnfPMa9Qcq93SzP20nK9ZPVTeq1dsZCM8BbChUbwJzKY/3iaW7OUlys4/cLjhzJ/udxTYOCAhgdVdNtLoB08Qjo7Xf5f389zrKlHm75eB6jYxoDQ7DxFmUSGo3B3Lk6mzbZVFRAe6eke1AjUgrRuE0sIckr1DFrdNp2StJDOm27ZkLchSWoRYQSOGDDgIACDYICuoEySI3AiRjsiqnm+J+Ow/9ZATdeocqfbzeJb1gLtdUqu1VfCz95AprbIJwHX7gLigvf05cyR44cl5g0WY7Sd6nD+KAyZepvup0HfwqsBl6SUi4RQmxgIquVI8e5qGY+YcpwyBKmFAMPee5MXmx/gv7EEBF/EYmRq9FLZnM83UTXgSy20CjKHyIcHsdXlGR8JJ/DLyykfmkrAY9Dbzqf4FCcpdJhfXk3w4la+kwfQS2J6bGokl3MSreiozFQfiXot7wvz23FCg8//3mC0VG47jrfabfddx/85CdQVGSycEGa7sEsQwNqDc6jj1rs3JEBJF/6kuqgH4/C3/07DA5DabHJlVfCgYMOxzs1Cks8/M3fWRiGQToFxkgWd9wg7ddhyAVLLeLmuIR6ofYspF3IaCAdtR+nQIAtiPWC4YVXXJWJmlEP/7oXvr4SfJN+G2gaNE0YhR5vhyOt0FANPf2weQ9svP78r49tw4M/VZm5T997KkOXI0eOyw9VIsy9SS+SM3xCYfoCKyulHBZCaEIITUr56oRNQ44c50SgEaHstGOvvuXjjSOfYGbVIKOjXsYzRRx2JU+XSbK1OmQgqQVwRwUFloPpceg/Uczh4RB5NeO4YS95TVEGxg1WpQ5yT4nDv4Xv4hnTpcrbzbLMAWJahGKZwpt+RTm6TzE9+G5ZtMhLZaVB/6ikuvL0t1J5Ofzu74Jt6wwO+vjaN2w2b84wPprG0TRaWlz+/u/jfPGLYTRNsG0vDI1CbRWc7BIsne/hgZVw5Lvw8qsusbjE43FwDR1HdzELdSgE9tqonaMC2iQcEjAXCBqq+7taU3uMDAHDoCHJRCWjzRkMYB4mR8YNemZD41lKpB5TiaWxKGQsVSacDskkNLeorFg6DcHgxb/WOXLkeH/xYTCb8ksdxgeVd5XBGhNC5AGvAw8KIQbILXvOcRGMxOG1I1BbaOJkKolL2OTAnjKLhN9FFy7Ygky/F7dYwzeaQqYFRsghmzBJ6EGslJdjuwvZOfdrbDUGub2inj/25/F1ltKcbaZYHALNj2Q2lY6L2nFzdlUQjSsPqNKLKHttO6Gz9Zgqr911FSxuPHWbECpLVFmps2yxjzdfS2DoYJoaIEkkJNGoJD9fqNLcO/4HCgVh9z6Xnh4L6YKbEejZFPmlfiqbDPb1glOWQa0PcyEuYIcGXahMVqsDjq6u1wJZidvqkPxxdOLda/LELx0q7/RQW+bhN9dC9RSTeCkLhhKwqxnm1CszVMuCLbuhqxeqyuGq5eB5x67CcBi+/FvqdciJqxw5Lm/S2Byh/1KH8UHlF1MdnK7A2gikUQue7wMiwHffm7hyfJTYe1KJEX0ioXTYhX6fzXDIQc+CdATCI3HzXTJDPoZFMZ6Yhd1hkB0yyAyZiJWSUQu6x4sIhiK8krS5xu9QhMEa/RakOA6OgyALxlwQ5065/OARGIvBtz4DRRfgozSegG3NUFuiMjvP7TldYE3m85/38OCDJm1tFq5r4/PBsmU+fD71j8/qJXCsVflg1VbC0gWqXBiPuhiagzcgSGc0THT0uEF4FBZXwu6kF6IpGDdA2jAqIKrDcQG2Dnsslc3KCkhr8HBMpaPwAhrM1xgIw5ujMLQPZiRgQQmsnQdeU63i+dkLsGgOrFkMbd3Q3AGtrXDgCERCcKgZegfg0xvPNPqsyW3eyJHjA0OuRDg1QogS4ItAPZN0k5TyCxMf/2yq86YlsKSUiUlX/+Oio8zxkSZrw5ZmKAk7OESxJPTICEN+G+kIkBJDU3/UhQlSF0iPRmo0iN1pIvJdZJ/A6M3ilEr6h3Xm5jvEUzCGQwE6aIUI/1fA3g8EwVx+3rgaqqB/GPwXOK7vMdQlloJUBkrPIc6qqwVPPlnA//yfOoODDuXlOr/92yGiUYGUks4Oyc1roaJSw+9TU3svPg/CkdiuhmsJdGwCpos/IhBZ+N1r4bsHNNqKvWQsC1IOkAbHBGdCQKWAFyR4NIjYkEpO7FTUwK9DvgY9DvNCkqP7BJ0G9PcqT6w1jQ7HW2262gWLFpiAwNBheAzeOgb11UpQFUSUyIrFITxhUtrTo3quysrO+pL8WnFdZecQCKhLjhw5TseHyVxKL3UYlytPAG8AL6G6WqfF+YxGY0zdvDWtRYc5ckxmOA4Z2yFjbqGFAzSnq9gzfCWjIoTwSaQpEEKi2WrNjGa4eO0MZjDDCGVqYwwTWS4pcVyJi1q0F5q84kErA8+NUwcxBXdNo2F7KvxeuG89PLsbikKwcdW579/UZPAP/1DA6KiLzyf4xS80fvlLiEYdQiEXKWHVKklDg8BxdFqP63zpMwY/+VmW7m4Lv+Ewo97H2rU6ZWWwaB5UlwrstMGoLRjudSGbBGGBTwImoIEuocVB6HFkQwC8GqRNiDrQYiPyNDp2Oux6VuD3CoZLBcc2Z/n/dvfT15vG4zM4uj/MlTcU4vcJ5jTA5i1KtOi6+jjZZqKzC/7xn9X1r33p1yuyrCw8+SYc7YArGuC2K1XD/sMPK28ynw++8AWoOrtjR44cH0lS2LzF0KUO43Il8M51gdPhfD5YH6DNZDkudywbMvYAzdk9aH1Qva+LF2oLES4YRSlc10S6GhguMgNekWGJbzulC4c5ljeX5rdmky0zEREXkdQor80y6nHYoJv09RlsHYXyECwoU87svw5mVsLXL8A6JhgUBIM6IyPQ0QHV1fDww5J77hFs3Zrm9dfTRPKhuEinqChCQ4PON77s59gxL/fcA93dGrouWL9eZd0qygWzZwh2HRVIy2VkKKRcQ1MZJbJMCa6E1gwyzwCfR30jAjroFoxnyaZMnvi+BYZgNC0ZCAQI+EdIjybQdQ1dt7C8STaG8/jcHV6KC+CqFbBpq1ognc7AutUQnMgMOc7EUmlx+nLpXwd7mvn/2XvvMKuu897/s3Y5fc6Z3isMXYgiQA31YoQKkiXZsWI1x103slxi+xff35Pcm9zk5uYXJXESW1b8c2zZlmXZliVbXUIVBBIIIXodhmEK0+f0tvde9491ECAGBtBQBPvzPOeZmX322nvtA2ef73nfd31fVm+FhkpYuRFaaqAmrMRVYyP09cHKlXDrrSd3Xi4uHwek24vwcDwthFgspXz2WAa5DSJcThqagN62d9CDOlNWt/Fw0RdhWEPqYLX50apzYArsmI43n6XW6qDG6kVqGnU1nRhBi3Uds5A5jdJym0BNltCQl9JMkF/0Q9AD6Q54Zw98Yb5K351KMhmbZNImEjEwjINvXCUlsOB8eHc13HiTRmenxbp1aYQmGYppbN9hcfVVGXbvDuI4gquv1rn4YuUO//jjsGaNsnbYvlFFaOIJQTCooSEZHtGwLR2zxCZvC/BYkJMwHIMRC/BAcYnyZfBKVUBmeMDxgWFhkSeW8SPMDLqAtOklkzZ4cZXFpxerqNiiy6GpDnoHoKocpk3af23NTfDlL6g6u5qao3utUnnYm4TqIAQ+gjjO5lXkzDRA05XODATUYoOBAUgkoOwYDWVdXM4GfBhM5zRtPHrq+Rrwl0KILCqXclRZPFdguZw0gl6I9WaYWDtCHhMhpGrr4gc5oGFnfSqrZUnq/P0sEi9TlI9haTob8xE8IktERClNF3FHiY/Eej9XTdVY1gMtJfsLrNuHYesAzDyFK447OtL89Kdd5HI2NTVe7r23gUBgfwGpEHDLzXDzEhBCZ+9ewebNgo3bBOWlGl1dNlOnwyeXKMFQV6dsDx59VK3O0zRYuhTOvwASaVj3noMM2OgIwgGdniGBv0iQ1SRJjwGpDISCsGsEDAuMPJR4gTyMCCWwhm3IpCDtABZS95Iu1gELenOs+j08XCH59l8EME2NrTtg5y6Y2AITmw5uVdPUePSvVc6GH62DvhTUBOGrs8E4zi/SsybC6i2wpxeqS2DmBAgF4J57YMUKla68+OLjO7aLy5lMGpv1DJ7qaZyWHG82zxVYLieNkhC05KJ095RSLoaZ5t1EW7YVBnVlG1DooVcz3Mk9kd/iDQyRkxoaNg2hvYhgAH/1Xi49by3svpI/Oc8kEIQH34AnetUH6ZUzweuBkfRYszmxPPVULz6foLraT3t7mjVroixceKgPxD5RWFUluOuuAN/9XoI9nQ5V1Qa33uKj8YBVeKmUWgAYDEI+r0RWdATa2yVbtlg4jkRoUFsn0QwTwyOQPvB5JYMxRy0LLIlAIASOCVEBNSaUAX0SuhMgC55aGGCPwFAccED4iOcCvL9WsGePj7Y9GqvWQHUVrFqjooXXH33Z20Ek89CfhoYi6IyrvyPH2R+wuAj+/FaIJSESUpEsUG2LmpuP75guLmcLjpsiPCxCiBJgEgd4/kgp3zjSGFdguZxUbrvpPP72X94gVSW5oHwlseIwb8URKPMKAAAgAElEQVQvwR4xEFGb+uxWbnaWU5ToId3sRY8YeBwvrYkBBvXZtLREmTUhxzXnSoIeyV8uTbE276DlA/Tv1Xk8DpfPhsZjsFs4EeTz8oPmz5omyOcPX4zkOJInn0yxZk2OxZ/Q8fp1vv5AmNqag5dMFxerCMyePSrlVVcHvb0QjUoCAYtsHqy8IJG2CYZMKotBBKA/o6OFgzgZG8rDyr7BQRW/+zQMj8TK2SAFSunu81pIKyd4UQ1Sw7Jg3TqdUEiwu1O1y/F5obwMdnfun6ftwNZulRKeXKOE4JEo9sJFNbCyBy6ug7DnyPuPhceE8lP87+/i8nHDj8E5lJ/qaZyWCCE+j0oT1gNrUZ1tVgBXHmmcK7BcTioLrp3D9Me20Lt1L/Vbh7m59TfcXPUEvUYlmqZT4ouD5iOp+xmsL8YjLLxoBPPlTPRM484FcZqLg4Tw8D5xVoSGiUwXOM1pkm9VkrbBL0Dm4IV3IRKEeZPAOAZ7l26SbGGEJoqYyPEtlF20qJxHH+1haMgiHNaZMydy2H137rR4550szc0GmmbQ1panuyt/iMAyDJXqWr4ckkm4+27o6oLvf1/QvVfDytuEQjY+v5eyapg2E3JZWLtFcP4FGsveTDKIF3ICDAemChiCgGEh6h2iXQKcfeJqn/OpD6QOZAEIh4NYlvZBM2jLhqFhuPaK/fN8bRO88L46xM3z4KLJR36thIAbJsL1Ew710XJxcTk5pLBYx9CpnsbpyteA+cBKKeUVQoipwP8Ya5ArsFxOKqap8bX/cR3f/48GBte9gYxGsQywnRQTr2wkqk8nMbCTbLkH2y+wsxqWVcweMZdF5+tMLK7CX/hvO4hFuU+no98gWJlHZgpeVEn47D+o1jN+P5zTAv/yRSg7vMb5gCw2L9KJgcYu4pTipYRjz1dNn17EN77hJRazqKz0HlR/9WHSaYmuq0gXgNcriMdHbW2FYdhY1iCZTB6vt5xw2Eso5DC51aGjUzBxko9QiUHeBtuWvP9OlnTKYlvK4KYlxbz1LuzsACujQYdEk3k8Mk5eMyiaYZLZbJF3gBoIl5eT2J3AGbQLbuxevF7JY4+luP/+AKYhWPpqjlwsS5HPZF/kvGsIIgGwHOgZPvrXzBVXLi6nEuGmCA9PRkqZEUIghPBKKbcIIaaMNcgVWC4nncYJpXzpLxfytz+voXvzDqrlAHUzmqBuBpppM1ztYKYHsIRNIl+LtOqYcI2fwdY2nsNkMa34MDiXEFdOS9OfztP3VoTqUsk5U+CdtwRtO1WhtC5Uluuhp+F7f3r0cxSI0bt3HgOlpR5KS8fOd9XX65imoL/fRtdVerG1dfSldG+8McSbbw4jPAZrt/ZSWlLHircTZNM2HkOwtwuumGJQVgovLc1CLsvEep29/Rlq/ILP3eLlj89L+vtgJO3grcgRCGgMJsAJZ/nMfVmebSvC1kzK/EG0KSXkd+VIDlhMaDBobpa0D9m8s92hc2eGf/w/g2RtyX/+2OYH/1bGnXdGuGIG/OJN8Hvg4imwaQCe2KLmf8sUmOEuVDoiUkqEqzZdTjJ+dM7lOPqFnR10CiGKgSeBl4QQw0D3WINcgeVySugtSzDxC0H2vDcVz64KYmaIfAo0zUGT8+nP9SOkw9wWL/Mml9JWHqOCIP0kiZLFh0E5Jl/y1HDnPMnqRnhaZIjasHtAw+z2ENQF6Rwkh2H5OthzNTSMYXrpReca6thKlEZCxxW9OlZKS3W+8IUiXn01g2VJbrstSEPD6G/N/mGHLSMRktJLNusQGoQ9wkNduU1m2MIQNn6vpKpSEPBadNk+Ngxp0Ohht9+hBbjyckFaQlsHmNEUkWqTTRtj5AbTmJ4A19/gpcLrYc1L0NPvMKRppOts3h0a5u11eRAanQJWrbLJeEIYJqRyDv/4YJQ774zQUAbfLbTNSebgByugrNDj8Neb4dsRCH3EOqszEdt2+PWvN7J5cz833jiFBQtcN1SXk0cam7WMnOppnJZIKW8p/PrXQohXUe0Cnx9rnCuwXE4JYXRCQcm8hX5uP9/Pnm7oHIJUVmOm2UJZURMz6jSKnQHyqx/H60+z9bzLCJtFFB/QuNlAEDYEW8syVEtoymk8XWWTmZJH2+ghnYR8BrZ1wTf/Ay6dAZ9bAoEjtCesI0QdoZPwKhxwzjqDz3724HNm8/DsatjYAWE/XDULdqTKyOsJ/HaOiy8KE4kINqyR7MoYmEGTyfWCNV2CHf0w/xKTjS9pRMskuTfT/PKFHHrEonVugKIZGsVlOhNKvASjaaq9foZ8OtvXOcTah7BrIqx6IwsdUhlTBTQIRGCeRXBbmo3PZ0jVBXFMC1vXkX5B2tnf/31fACZjgSWV5ZYQYKfUNldgHUp/f4r16/uorAzwyittrsByOalIhNuL8EMIIcJSypgQ4sDQ3vrCzxAcuWjNFVgup4SZBKjCJIhOyNQpbYJZTQfuUagFeO8tzKFu5thZJtRLQvWteEa5CQxIhyqhofvgwomC5d2S9DsgNMi0wt4J8LIGa19XFgdf/+xJucyPxB/ehvfaoK4UUln4+8egOmyw5Lr9S+TiCThnto/VG8C0YdsqQcoGDYdVjTZRK0luRRrkEGBhD8LWV8tY3FrHDZfo7BmKEGkLsXFjlC1bUvgjgoELfYz8wVB+F61C2WcEpPLPWpcmaejk415CAzpZU8ex8pimYPKsCNms8sMaycLWEdVnelqZShMiYXoFlPpP1St6elNa6qe+PkxXV4zLL28+1dNxOcsIoDMLd/nth3gUuAF4F7Xq58DcvQQmHGmwK7BcTgkCQRVHEcaomABtb6N7QpSGm+Aw37Bm6wbvWBYhIThnhmReQuOPGyDvga3TLSKGhLxBvF7w1HK46wYoOw3uJYNDsGyV8re6eD5UF3qtSgnrdkNjubI5KNKgo1f9fSBt7aAJjfIQbF4JlDgQtHF6E3SvS4MtQA6i+pM6gASrj/de8XLBDVWE/IJ0wsAw8gQCGrlKPyMrSvZ7K+hABWrlYcwEJwh7OsnpglhfEZpuUlbp47JrYOJEH8k0ODo8vEmJLMuBRY1wQZ26pgklyr7B5VA8Hp0vfek8kskckcgRQqwuLieAFDbvET3V0zitkFLeUPjZcjzjXYHlcnpTfw4UfxN0E/yHN9O9zjApF4K90qFV6FCvkzkPunMOO0IW6aDEM6Rj5gWWDdHEwQIrl4ehqPJ1Kj5JHTjTafjxo8pkXddg0zb42uchXKTSaX4TMnkIeJUdgmWrwvEDCQUhb6m2gggbjCykpVIzNuDsa/wu1EM3IRIhgclgu8M3P62xzobNm4IkknHifYGCjQNKjwWAIpQ9VgTY4QEtCLaDlUqDsBi28yTjFZSXCsIh2JuBWBaaiyCRhy3DcGX9yXlNP+4YhuaKK5dTgsQ1Gv0wQoi5R3peSrnmSM+7Asvl1JPtg9j74G+G0KRDnw+NvbLFEIILjf0r7zpCqsfx5HKN8u0aiZDAjAuCvdBcAUVBJVgMHQZH4Kd/gJGEak581QK4csE4Xt9hGBiCRBIaCqU2HV2wt08JLICbL4BfvKbEVt6CqmLl63UgLU3q+ZAGG9YmIZZXRU/oypU96FPOm9kEpEZUD0JdY840iZkWdO6BUAhKS4uYv9DkPSFIjAB7bCXUNE2pv2rUHbjNKYg2DxACKbCsBH17He75pI5hQJkPijywOw55B647hrY5Li4up4YAOnM4Ci+bs4t/Kvz0AfOAgsMf5wJvAwuPNNgVWC6nFmlD50/ASgKvQfMD4P3o6/gbqqC+EgZG4N4WD6t2gJ2H8hqYMgFuehC6ozCnBC5tVTVODVVg2/DSSmhtgMajbFR8vISLVI1YIql0jJRQXLi/SSnZ+HaO93+TJhqXXHOdhzuv9PPeTo2Gyv3H0DSY0KxSi2+8JBhIAoZQ5qBhP96giZ3VsXwaWFk8AYN5swwuvTSEJyDYthO2vgvnnAMDvT5Kem32LB2GZAJyGjh+qAzAHB/EgQk29O3zvLKBGE4uz5bVu3j6DxXcdVcEvwFfnA4bhyFiwoxRmivHs/BKu3Jxv6TJTRu6uJxqUji8S/xUT+O0Qkp5BYAQ4jHgi1LK9YW/zwG+NdZ4V2C5nFqkBDsNehDsKMj8uBxWCPjT6+BXL6jGv7PqVAuX+mr4/lpoC4ARhOdysPlpuP+Tapyuq0c0MS7TOCKRMNx5Kzz1giq8//RNUFmosVq6NMeDD8YpKxOUFwteeCqNzFsEWsL0DgvVBkeoKF0uDz0j8I1vmDz2G4euqI+ikM4lc8D0QlWph66+EHfeUsz2PbCtQ8MX0kkkobkGthauGwM6Xh5EqTQP+wQUvSF4PEdkogYNDk5FGCdrk8zHIC2AIuLRPPfdN8D8+V6mTfPh1yDeA50pKJsOtR8KQr7ZASv2qKBYfRgmuvY7Li6nHDdFeFim7hNXAFLKDUKI2WMNcgWWy6lFM6D2szCyHIKXgq923A4dCcGXPgnd/dAbhU0j0JuCdh94M+DVIeWBARO6B6CpWokVR45/AXwiBWt3QGUI2nZI2todzp0puOh8jW995dD9ly7N4veDVig2Ly7W6dpt8+WbLN7vMtnRpaI+qxLKUPXT9Rk2roxy9XmCUMRh8Q0RdEfyyipBLKVx8QKorNJpaYHpe5XL/YxWmNwEu3bBb1+DpJQkkw7gRRVdFVKNxCFZwpwpEqfIoPVWnfaBPG+9LMikiwv7OiQS8OqrWaZN8/HcGnhnu8pQ7uiBby0B3wH1Y6WFMiOvDkXHYTW2bh1UV0Nl5dj7uri4jE0AnbnH2RrsLGCzEOLHwC9QN8bPApvHGuQKLJdTT2jS6LVXByBTKWR3F6K2DhEIjLpPXwp+sQ0aQnDrRCVAhICKUniiDfoKYkRHlRcJC2xT1THVlkPHXpVyu+UKqB1nt/HlG+CRP8KGV202vhfFtizKy3T++1+G+Np/O1RhSOnQ3y/p7c3jOEpgTZgAJpLPXQcDURiKQWU7lIWhMeaw1RA0Nhps2ZLjj79LI6XANOHeu3yEIhr/3yPK/+vbd0kMQ7XkcRwYNGHWAggHBVs3++npH1JLAdGAHGDjMQXhCkGJJbn1BsHf/CCPtGKgJVWTROmAlDz2RIpPXF9E97BGWRiKfNAxAMkseE1JPA7BIJxfL6gpUhZbFcFDLv+IOA688grMnXtyBFYyaZNIOFRUGB+0M3JxOdNI4rCa5KmexunKvcBXUD0JAd4AfjjWIFdguXwssH/1CM6uXWjNzRifHyXkA+yKw5449KdhcRMECzXv7SPQE4fmEvX3Z6bDr7YoB4MGE37+JZhSr6JMXo96jDfTmsCfg+2b4tjZHOgaA4MWv/pVknvuNIlEDg7NL1rk5Zln0jiOURBYDpGITl2dsqkoj6jH5Aa1//Cwl1Wr0vT0WFRV+RBCUFGh0dXlsHOHxRVXeLhgpuT152Ocf36SVErw5S97uf+BUobi0FSoN7vqCi+PvSex0vvm4wAeZl4omOJJsXxtlh/0maR7+6mfYtDW60P2ZSFQAh7Y3Jbnwf+0uPoaDw89A/mssp8oDsAzz9gsX+7Q1KTxuc/pNBUfXqzYNqzZAl4TZk46uE+hpsFXvgLm6N2ExpXBwTw/+lEfiYTN/PkhbrnFzWW6nLk4uF8gRqPQh/Ah4Fkp5dajHecKLJePBXJkBGGayJHDt3I4pxQuq4PawH5xBWDLgz+gFzRAxA/XT4WF9eApvAvCR2HeLgsNCo+1VVxjFSy5FB572AZNIIRWaK1qkx+l7OyCC7zccIOX117LoOvQ3Oxh8eIAkYjOwDCUlxy8f0mJzgMPlGJZ8OabFi+/nCcSkWQykuJiFcmqCWT42X/GiMVUyOg737ExzSFap5aybpeK+LW0ePnOXwR59500O3dB74iH4kYDqzHHa2/1UloaYc/uHIO9aa5b7Cf9vp/u3gqIOsAIA/1RfvC/hlmzqpIrrirG69NJ9MH7myQrVkgaGgS7d0sGBqD2CNngzbvg1y+qBYxfDUHTh/b1nvgORgB0duaIxx0aG728+26Sm28ucfsEupyRBNCYd5I7WHxcEELcBPwjqji1pVB/9T+llDcdaZwrsFw+Fhh/ejfOxvVo08857D5BE24exVe3rki1ahlOQ7EPhtKqN975tfvF1dGwsx0ee0qlqG69HqZPPrZruGQhTJ/h4f01ecAmFHS44ooApaUHf2A7jqStTXLjjSGuvTZAKAQTJ5qUlur89mV4ez3cfSOc03rw8TVN4PHAwoUGw8MO27c7XHKJydy56iJ377ZIJPZdsAUYPPhgjI1bSlmxA6JJ+PrNgimfLOPZZ2M8+2yUrUMC3R/HIw0mTdcZ6PUg8xbRgRSvv5Sju7sW8hLYC2QBP2Cx8sUOunYM8OffmIARMdm1R3DxxYJlyxxaWjQqxkjBBv0qkugx1O+nioYGL0VFGnv2ZLnwwiJXXLmcsSSRvEP6VE/jdOWvgAXAawBSyrVCiOaxBp0wgSWE8KHylN7CeX4rpfwroe5Qfwvcjlqm9EMp5fdP1DxczgxEdQ169fH5JoR98Lm58JuN0BGFmhDcfg4EjyEVKCX86kkI+NWKu1//Ab53P3iO4RjFEXj80RD//K8a7btyXHWlyVe+FDikrue55/K8/nqObNZG1wXf/GaQ0lKVGjQN5d1lHKFlmNcruO22Q0M8555rEgzaxOOwz0m0slIS8KkUaX8UVm2Hd7YZTKrz09CQZtYCLy9t99DVlSM36KHYL/CIQcJhP4ODJpotcJCoWq19Dq0eQNDZPsKjjw0zY14ll50PCxcYXHqpJBCAaEbw+haIZmB6FcxrODgq2FIHD9yhrrPkFNbdlpYafP3rNSQSNuXl7vdRlzMb+yStIhRCNACPoBz2HOBhKeW/Fnr+/RpoBtqBT0kphwu64V+BxUAKuGefyacQ4m7gvxcO/bdSyp8Vtp8H/BT1re9Z4GtSSnm4c4wxZUtKGT3WL1gn8o6RBa6UUiaEECawTAjxHDANaEAte3SEEO46IJcTTl0YHrhQRZ+047iHSKmsFMywSqXZtjrWsdLcKPjXfwoCo1d2O47khRdSrF7dR1tbDk0zyOeL+f73qxBCsPhiiS+XYNnSLLsbPFx+eQivd/8F7SCKg2TyKD3FZszw8aMf+fn856OkUlBSkuKRR1owdPjKYli1TTWXjgTh1S4NDcmubVnaVmfRhUMu4CefdxACqqr8xOMG6BZDGQ0wUYtrlB80mEhHMhKVJHIwqRBZDIUEqRz8eCUkc8qZftNeyFqw8EPRx4oPpUFPFX6/ht9/dixfdxxJb2+GkhIPPp/b+PdsIojGAkZfQHQCsIBvSinXCCGKgHeFEC8B9wBLpZT/WwjxXeC7wHeA64BJhcf5qALz8wti6a9QJqCycJw/FATTD4EvAitRAmsR8FzhmKOd40hsEELcAehCiEnA/cBbY13kCRNYUkqJahML6u677w78FeAOKaVT2K/vRM3BxeXDHI+42jfuluvgiWeVjcP1V4HvBHQ0EQI2bx5i2zYH0/SSTjs891ySLVuU/cEbb8R5ZWmM8nKTnTvjxGIWt9+uCq+z2LxKNxKoJUiIQ6vAP/OZMj7zmTJisTzBoI6uqxck4IXaUkku6zCCzoyJOr9/IceqVVlyQqO0MURztU1VOMjQUCUbN/bh81kEMiNE9Qi2raOElRJXECNQ5OHKG4ppbT24vm3PCIykoalQL+4zYEX7oQLL5eTz3HPdLFs2QGWlj/vua8XjcUXW2UISh7fJnJRzSSl7gJ7C73EhxGagDlgCXF7Y7WeolNx3CtsfKeiKlUKIYiFETWHfl6SUQwAFkbZICPEaEJZSrihsfwS4GSWwDneOI/HnwPdQgaNHgReAvxnrOk9ozFsIoaO6ULcC/yGlfFsIMRH4tBDiFqAfuF9KuX2UsV9EqU8aG91eGy7HRhKHdvJUYVB+mAbRB5LJwdPvwrYuaKqAJQsg9KHan9nnwNRWFbk6jFPER8ZxoL5e8O67GiAJBiWmKRkaUtX1mzZlqK42CQZ1QiGN9esz3H67GutFZyHV2EiCY7y1w+GDxZfjSN58YZDMxixTFxRxw7wgL4V0Jk704vdDZaVDbS10dg5RXGwyb16IGTMEvb0eli932LhRI5FUVg2BQJLG5gD/8H9qaJ3mpaYc/AeIUb1gkLoPywH3c/z0YNeuFH6/Tn9/llTKdgXWWYREjGeKsFwIsfqAvx+WUj482o6FWqY5qNYzVQXxhZSy54AMVx2w54BhnYVtR9reOcp2jnCOIzG98DAKjyXATaiWOYflhAosKaUNzBZCFAO/L9jLe4GMlHKeEOKTwE+AS0YZ+zDwMMC8efPkh593cTkSvydBO3kCaHyZCIExbhwvvg9rdirH8S1d4LwNd15+6H4nImp1ILouuPbaEO+/H2NkRC0vnD7dT0ODEkTV1Sbr1qUJBnUGBy1qag4WStM4vrxaKuWwY0eWxlqdZH+aSLiI+nqTnTstdF1imoKOjjx+v2DePJNEQsc0BZdfHmb9+hiRiKCy0sPgoENRxMs//0sNi64Z/TVvLoWJZbBjQNWU2Q7cNe+4pu0yzixZUsfLL+9l6tQiiotPgF+Jy2lLEI3zGZ8VJT+AASnlmO9qIUQI+B3wgJQydoQap9GekMex/Xj5Jao1zgZUmP6oOClVm1LKkULIbhFKSf6u8NTvgf86GXNwObtIIvEiyCGxjmL/3X1QEVar1mpKoH0cEtd7GaCHfibTTPAYblyf+lQ5gYDJM8+kCYcNbr+9iMZG9VadMiVEW5vN7t1ZqqoMbr99fAqVQiGda64Js3ZtiuuuC2MYgm9/u4L+/l5efjlBe3uMkhKD2bMDZDIOw8M255zjpbdX0tiosX27pKzQc7C6WvCJqw++v6ULi5P8flW4ftd82LhX1WE1l0L9ODvnuxwfDQ0B7r3XzdWejSRxWCGzJ+18hdrs3wG/lFI+UdjcK4SoKUSWaoB9d+JOVO32PuqB7sL2yz+0/bXC9vpR9j/SOY5Ev5Tyj0d9cQVO5CrCCiBfEFd+4GrgH4AngStRkavLgG0nag4uZy+3EGQNWSZgED6KsHdrDbyxEaoF9MVg5jhkpdezjSgJvHiYzsSjHqfrgiVLilmy5GDVkclIfvUrG683xF//dSmmOT7h/La2NCtXxigtNfjKV8o/KG7etEmybFmWvj5VlzEyksNxHCZO9DB5sofFi0O89RZs3+6jtTVLW1sOxxHcfkcx//KIwNDhtk9AWQT+7d/UuR54QK289Bgwp/5wM3JxcTnZSATSOWmrCAXw/wObpZQPHvDUH4C7gf9d+PnUAdv/W6Hp8vlAtCCQXgD+Tgix75vmtcD/I6UcEkLEhRAXoFKPdwH/NsY5jsRfFVrlLEXVYQFwgDAclRMZwaoBflaow9KAx6WUTwshlgG/FEJ8HVUE//kTOAeXs5RKDBYdw3/vq2aqbi/bemDeRFg056PPYRJN7KGHWo5voeyGDdDQAJGI+tvnE9xyi4HPx7iJq4GBHP/1Xz34/TobNljEYhaf+lQVliV56imLgYEUmiYwTVXEPjiY57bbwkyZ4uPNN7O8914WwzBZsKCYmTMdLrhAZ8ewiWlBOgO/eR6++hkVuYLjX2Tg4uJyYgkhuIDxcfD90di7XAzcCawXQqwtbPtLlOh5XAjxZ0AHys4J1CrAxcAOlE3DvQAFIfU3wKrCfv9zX8E7akHdT1E2Dc8VHhzhHEfiXmAqagXPvhShBE6NwJJSrkMVrn14+whw/Yk6r4vL8eAxYfE89Q4eL5qpo/mDuspjx7KUHcSBLFgwvm/ZoSELKaG83CQU0tm1S0WrpASPR2CaGvG4RS4ncByHfN7gxRfzlJebPPlkipGRLL29Fn/2Z8VccUWA4mKN//XQfp+uvKVaFd53n/r7QIElJbzyOqxdB+fPh4UXjuulubi4HAMJKVnujNJW4gQgpVzG6HVSAFeNsr8E7jvMsX6Cyoh9ePtq4BBnainl4GjnGINZUsqZxzjmJLmKubh8jHAkbIrCygHYPUbv02z2+PywjobZs6H0BLe+q672EAhotLdn6OzMMHu28lMwDCgpkQQCpeTzGpbl4DgayVSEf3oQXnwxx5YtCbZsyWDbkuXLY6xcqVxZPrUIkmmwbLjtWnUeTTs0etWzF15+DTQdnn0RBgZP7LW6uLgcHonAsbVxeZyBrBRCTD/WQa41sYvLAUgJT3UqcWVqYEn4VAPMLTt4P8uC3z0H6zZBaQncdStUlB16rBXDsGxYfZO5ohzOi5y0SzkqwmGDL3+5ji1bUoTDOtOnKwPUTZss+vttpk71s2tXA+gShAGan909Gv/xgwQTztWZVuylvkLD64Vly5Jcc00Rk5oF3/vy2P0avR4V6YpGVQTxSE22h6Pw1nsgNFg4d7+vluOof4tjcdQfjdF6TPb22vT22gSDggkTDLdNjssZTQjBRdr4rBw9JJz08WchcLcQYheqBkugAmunzqbBxeXjxnAOVg9CSwgQkqRt80yPxtyyg7+Vbd0Ja9bDhEboG4DnXoW7bjv4WO9G4aleqPWpZP3jPeDTYEYRpxWlpSYXXXSw8lu1Kk9JiSAet0EEwTT3m1cJwdbdOtYkQbRcp9kLUkqE2C9QjkaLlJXB3XfA9p0wfSoUHeZ1eX8d/N2/Axo0t8LODrjvDhgagZ/+FkbicM3FcNkFx3f9G7bC719Qv9/yCThnCmzdmuORR5KAxLbhoou83HhjwBVZLmcsCSlZlj+aNddnJYuOZ5ArsFxcDsAuaIisyNJON2lhk3A89BKiiv35OttRIkIKsH0wMkrpwro4lHnAX6hHihiwKXH6CazRyOdVz8XaWhjNPsYf1gmUGgz15Og3NTJxh2uuCR3SV3EsWieqx4F0dKloVlUFPPkS/OARlT6sDkHnDjWvTBZWvgfxJNRWwovLYMHsgw1Nj6pBg80AACAASURBVIZkCn79DJQX1iD9+hloaYCnnkpTXq4RDGo4jmTlyhznn++jqso13nQ5Q5Gcqem9j4yUcvfxjHMFlovLAZR5YUKRw9L4IF5DJ5cPMLdmmNV0cAXzCKA+wSc1Q7QJvu9ApgQqK2H1Wvh0DSyuVOIrqEPXAfVZWQcCJ/j+JSXs6YRMBmprIBQae8xozJ1r8PjjWWbO1Pjd72LY+VKVMwWwLabUWXzm+iK0aB5fxqK11cvcuR/dpNCy4D9/BVXlsOgq+M1S1bQsrUE0DyO74aKFSkgVh5UD/8AwBAPKtHQ0bFvy8suS3bsll10mmDJl/z9CNqfSjL7C4inHUdtSKYfKSh3HASEE+Twkk67fscuZS1AILjbGRxI8Mi5H+fjjCiwXlwPQBHyyOUffQD9ONkJNcIRJpVmiSJKkPxBYvxx0eL3RITosyebBSYHQTf4mDmkbbquFy8pgaxJ2F0w2gzpceIIbGD/zPCxfAbqm2vl84V5JKCTx+cQxpbdmzTJZv97i7bdtQqE00WgUHA+mCV6vjd+Xp3hY55OfHDscNzAMm3dCYw00jbGo0jDgjiVKMA3EIRQEYUFIQiIJ/hB09MPjL8CkahXVCobhpkVq7Ghs3gxLl0rKy+GXv3T47ncFgYB6LUoicO5UWLtJ7VtXabF2TZaSMpNfPqEh0Xhvj0Ym6+WfH5fcdLnFww8alJeNfi4Xl48rCQlv5k7Qip2zFFdguZy1xLEZwaYYnaID+hWGdINzq+KEsDAwcJA4SDyF5sl78g7/PJSnS7cxShOIFMT2mmTjOpUN8M+9OjfU+Kn2atzXBNuSqsh9SgiKD+2/PG7098OKldDUoFbste2y+c734tRWJqmt9fDZz5YSDh9FX8aM5K//Os4LL6TJ521ME5qa0gSDNgODkMvl2LrdoKNzzENh2/CTJyAaV3P6+l1QOoZr+7RJ6mc4DhVhyYvrHAZ6lfi99iaNlnrB0uXwi3aY0wJDe0Feffjj7Stg17T9v+9DCLh9Mcw/F6JRmyceH6FtAzz/pg/h97C5TSBjDmgOtga/f16jt9/mhcf1444OuriclkiQjpsCH0/chKvLWUkXXfyYNTzGFn5CP93kPnjOxOBcJhEjyTAxhonRSj0R1CfqWxmLuC1BWnhyeVK7A9g+B2P6IPlgih49xg8Tw1hIyjwqanV+yaHiKmPBun5Y0Q09iY9+Tfs8s/bZIezalSU64tDY6GXv3jxLl8aP6jh33BHlH/8xybp1ki1bIJFwiMUs8vkcjpMjHIaAX8MfGnvFkW1DIqVW/VkWZI/BZicclOjDDsUe5e6XTQpefVrS3QGGhGgWampVWm/v3sMfZ9o0uOwy1UfyT/5E+yB6tQ9NU4sVaioklgXxjEY6A0VBDZmXKhyo6+BRDbjXbIRHfzf6uUYjkbDo7s6STttj7+zicsoQYI/TwwVwI1guZyG9rOIZXiNJhGoEaaazAi+3sl8wNFBFhBBJ0njxUEoYG4dOeugx9jCjJkvHSISB7gryxQbB+hHIGUhMPBnYEMsxUJSnmtFFSCIH/7ke+lMqMuMAt7TC/Orjv66KCmhpgrZ2ZVswEnWYc64K+QeDGsPDY68QikYdnn46i+NoGIaGlDqWZQEWXp/E4xV4vAZTzwlz+SVjh+M8HvjMYnjzXVg4B2oqjv56kknIZSVSamQzyi8rn4d3lsG8S8G2oHOPEkg1NYc/jmEIrr9+7G/m1dUGN9wQ5Ilnssy9wGTrJk2pt6CEWgEZCf2g6ZL1W8eev21Lnn9+gBUrooUVloKrry7lkkuKz5jViFIqUWqaZ8b1nM2EBFzqHZ+Yyy/H5Sgff1yB5XJWMcAeOnkRG0kUB0kMwVbqmXbIvmGChFG+UBLJOrbQRR/1uh/D8VMcilLcMMQmZyZpQkS0DEEHNAlWHsRhjYphdS8MpqG54I6Qs+GZNji3HLzH+a7UdfjsHbBmLcTjMP88nTdfz9LRIbBtyQ03jG3ClUiAZUk0TSA0kDZIaRIOa3zzG2Hq6gyCRV7q63Uaj7KX4LSJ6nGs+P0QCgkiEWhugc4uKC5TLXjiafjze6C+FCZMgOqPIExBRdeWvQdDaT8P3O9n5Qb444uwdUhiTSn8O+oCSsDXI5h9FJaDK1aM8OabIzQ1+dB1QT7v8MwzA1RUeJg2LfjRJnwakE5Lfv5zh/Z2yYUXCm64QTtjhOPZSMKBN1KnehZnFq7AcjmrGKQTCx+TeYc9XE6UACZ5GukFDm+bPkKMLvooIYIwBcUiS5W3E80/RMCf5JXOxeRsgfCm8OoOs3w65Rw+atIZhwMzbB5dmZrGclDxEd6VPh9c9IEflJ9pU8rp68tTU2PS1DR2n7GSEkGoyCAes3DyFBwaHHr22jz0cIa/+n/D1E/QsWyV/tNPYMmGYQjuvltj2TJJtSGZMlkQLhPEE3DH9bDg3KPz2zoa3lkPzy2DgA+274a/uBduuBiGgyYvrLXIpwTo4KmDy1s17vr02MdctixKTY0HXVeTNE2N0lKDFStGTqjAchxJR0eMZDJPIGDS2FiEro9/Nci2bdDWBs3NghUrJBdcoKKoLh9jHFcgjyeuwHI5qzDxMYCfELUs4U0GCeHjUrJ0wihRrH0MEkVDRyCQQjKv6F32OgPkhMZF/uXsHJhA5+4mfLpkyYx3mVMBGylhKvPxcKg5U2sJbByE0sJTyTz4DSg5Rh+nsZgwwcuECUfXwDWXk/z85zbXXu/nqacSWGkLlbzMk8tqvL0qw333C5Z8zYMvqDFnInzq0v3j83klunwHXIONRD9CJO/QOahj7GsOXV8v+OEP4Sc/EUgpEAJuuw3OHcU/ubvbprvbobFRp7Ly2ARFLAleE8qKoXdw/xzu+TTUnmuQ7IWUDSVV8O83Kt/VsUinbcLhg1PEpilIp0/cSq329ii/+c12RkYyCCGQUhIOe7nttklMnDjG6oJjpKhICdzeXtWIPBAY18O7nGRCGlz60Z1WADdFuA9XYLmcVdQyie2soY86TFrwESJIDbIgAhxsBthKkl58lFDJdHQ8aKgCZ0Weeu8Q29JFOLZkqL0U812HEoYpr9pD0BukTPcSZ5gOttLKrEPmMbcSNg/CjhHVc8HQ4I6p6uepYtcuSXu7w7WXezl3msHjj6fYtjVJ3tHRdB1HOnR15vFj01SlsWorzGpSdhCvLZf84pEMPXtztDQK/va/e4jOSLJexGgiwLVUYIyxpqajA376CORzsGQJzDtPbW9uFnz72zA8DOEwREbJdPb1OTz0UArLUh/2X/tagEjk6F/MC86FbbuhbxBuvGy/gLqmBTpT0F+hMoSfngr+o9OrzJ5dxNq1cerq9g8YGMizePGJ6ZfU3Z3gxz/eQHGxl6am8AfbE4kcP/nJBr785Vk0NIyfy+2ECYI77hB0dEhmzdIIBt3ox8eZhA1vjMNiG5f9uALL5azCR4j53Mi7vIqGjpcQaVJMZQ4Ae3mPXjbg4GCxmRQDTOAqKihhMw4ODgKDsOGj2BvFxmL7tgspLhsiZMTIDhr0xT3Ec+D1+EkSG3UeHh3ungFdCUjlob4IgifQwuFosQoL3YpCGsGASShkEo1lkVKiSwevz8OuqGDzGkgOw5+tg/d2OaS6h6FTQlyyfp3B85vy1P7bID6PyQRvgrZomLvm+z/oITgaby5TKcfiCnj+hf0CC5Rh6pFsEQYHHSwLmpt1du+2GR6Wowqxw1Echvv/VNW0H9iUOuyF+2bDcAYC5rH9G115ZSm7dqVpb09jmoJcDlpa/MybFx578HHw2mt78Pn0Q6JmoZCHTMZm6dIO7rlnxriec+ZMjZkzx/WQLqcS1wZrXHEFlstZRymVXMAiumnHxqaSGsqpQSIZZBsZEqQYIk+WQaeT1/KDDDolTNAakd69GBjoYiYV+pvERAYrbNDfVYWuO1QHBvAX7yYna7FJ0ELTYeehCRjHgMJxMTICjz8OW9qgD8FAl8bGXQ7Tm2H2bJ2SEg/vveeQTFloHpPwwgCrynSSQ1BZDpu32+SzaRgpUhdUlIdsEulPkbXy2EGHdSkd7TUdpxe+ctvhHddralTfwUQCpkw+tutobNSpqNDYvdumtlanpub4QoHaKMMMDSqOI/0ViRjcd18D27YlGRy0qK720NrqxzgBYcpczmbTpkHq60f/D1Va6mPHjmFSKVWX5eLyYUIaXDpOpYFuilDhCiyXs5IiIkz5UOpOIBAYJBnAIkeKJDul5A3bJG1a2LKHW3JlXKFLcultzMtLHjeaCM2OU+4dwMxpzGnK4PHYRDw6FUzHn5tIrwPlXpViOt148kno7obODPT2CxbM0UlkNKZMgj+90WDlSo3f/95g54Ckp8Gk7AovmaSgzYB1Paii2JoAJIE2CbYJdhAZg8w6Db3EIbM8yLNPQvQKh8UXabQ2jj6XSy9R6b90GubOObbrCAYFX/1qgJERh9JS7bSxDfB6NWbOPPEq2rIcpOSwvSDVdoFluSEKl9FJ2PDG6AF3l+PEFVguLgfQwAX0s40Y3eyimk3UMGQG6MzWYjlefqt1c33yeWY6Xjr85Vzu9BPK2aQm9GHkbMJGKbOCNcwVl/N6P7zYp447IQB3NhW8Kk8jenuhtBSS/VAUgExW0Nwi8EVUbdWVV3q55HIvf78VehOwOwkDAzAwArQDWSAPNAE7gbQO6FhtBvH/iCOCWfKbDRA5tm/UefE1aL1r9LkYBpw39/ivxesVx92MOZ12eOGFOIODNldeGaKlZWwT1dMJv9+gtNRHIpEjNIoBbCqVp6jIM+pzLi4f4HrhjiuuwHJxOYAIjSzgS+zgdbbSg24Z7Ek34NEsvqr/O5OS24ln+7H8f0K2ew9runSysV6apmZorMjhCdrUea5iJKfEVZ1PpZi2J2BjFOac4F6Ex8q8efDiixByYE9StfMZjsMnFuzfJ21DzoHJxbA3BYM5panIO3xQ+++gUoTZwiCpY+2qBGcEsMBIY+kmw8MaHMOqwpPFa68lePvtFMXFOj/72RDf/W4lPt9ppoaPgBCCyy6r5ze/2UYgYB4UyXIcyd69SW6+edJhI1wuLiENLh2nYKubIlS4AsvF5UMEqWAGS9jGE6x3QtSZe/mS+RCTne3oNkjNImot59XNRbRtnUzxeXn2Ziwcf5oFqS4i3q1kZAbwfbAqUBOQk0c87UknFnPQtBwtLTArYuCvMBAeaK2Hcybs3y9kQJGhNNS8EugFuh1wqoDdqCdyQAKlnaQEmQSZAwor6KQkF4vhN4pgFNuK4yFbEHPeo1zVdyQSCQe/XxAOa8RiDvm8PMhu4uPA3LlVdHYmePvtHoJBk0DAIJ22SCTyzJ9fzfz5Vad6ii6nMQkb3oie6lmcWbgCy8VlFDQ06ikhJDxcar6CR+QRQEgmkEKjhwT1EwfJrZ7OjleKmfOpDshodGU1Jlrv4PX0UxYKsjZuEsJPsWEw+TRqDhyPOzz0UIJYTGIY0NGR4Z57gkyefGgBtCbg+mp4dA9ggyHUaj/HDxQBMWA16m7iA5wcpHNACsgUjuKnxO/nuadiLJglWLjwo6mid1bBH58GJCxeDBdeMOaQI3LZZSF27crR1WVx9dUhioo+fk1vNU2wZMlEZs2qYPXqvfT3p6mrCzF/fjXNzRE3euUyNm6J3rjiCiwXl1HQ0JnFhSzRe3nODpPQgnSJaoKeNmQ6hKml6GlqYcK3sphWDKcoQDpjk9aCLNfKCYu1TGoowhP3Y9g+loSmUOI5fUIimzfnGR6WtLQoITEy4vDKK9lRBRbAORH4kgkrB2GjH0JbYHiTptKE7UAUVb9RDThZ5T+Ry4Iw8HjByifp3pkjPWTw5/fb/P3fhVm0KIgjYV0cVsVUGnJOEcyLHLlWLZmEP/wRqquU0eUzz8CM6coj63iprDT41rcqsCyJ53QrlDsGhBC0tERoaTkxXlsuZy4hHS4dJwcRN0WocAWWi8thCFDE9WYRCedPWEeOtNZBt3cBd+VG6BTr6PHUUB/vZkb7ZrS8Q19xBeurpjPdSlLkdGF4ZlIRMRlhmAQ9QMu4zi+ZVM2Uj8ZV/MPYNgixP2dpGGrbkWgKQIMPNhvQ68B7DuQymhJWQSDugHAgZaFyhgZC10A6OLaGY2fp67NJpRyefFJj8mQPG0Mmy4ah3KMiZX/ohy1JuLtu/6pLKcF2wND3z11KFUUTQpWAjTX3o0HTBB6PG+UZL/J52LABmpuh5DSrPXQ5lIQFbwyf6lmcWbgCy8VlDD7tredTuZuQ2V+jaTYES8glKynPDDN9+ybyXoORijDF+WEWRV+hVNYSEMMIfQt9xbeg6xo5VRY+brzxBjz/PBQXw+c/r1YCHguTJhl4vYKuLosdO9KsXZunttYD6Nxxh/+wH4j9cdUz8bNXQ0UIli6DdCnQA3g16HGU6rFAMzR8Pkk+61AozkIIST5vsWxZjhs/Y7Gi1qQloMQVqFqv7SloS0GVDo+tgnd3gF+D1kq4aiZMq4dLFsIbb6oxF180vh/gORtGcjkM/16KqS+4+LscKzt3wkMPwXXXwc03n+rZuBwVbopwXHEFlovLUSDyv0do5SD8oO+ipPSLLOz8MalAmr11TUQpos5ycARkPJKkSBPJ9+JLvkUufD4VxIjyCF5m4OO8sU84Bi+/DHV10NUFmzbBwoXHNr68XOeLX/y/7L13lBz3eab7VOycpntyHmAGaZAjCQIkGCEGQRJFSRQtWZZkW/GurfVdy9d3vT7r9XrlY5+VfY/WtK1oURQVeESJEnMECAIMIHIYYDA5d86pwv2jQAFEIgiMCAKs55w+k6qrftXd0/X2F97Py3/9qziP/NpBterk2IDJK68UOHRI4FvfOns6UxKs6JGqwF0bYGE7PLENhlygHwdDlRBFD5qWJBI2kGWBaFSgWtWQJBNRtEaqpNMGiZLVhHh6aZAqQn8O/scTcHQMfF6gDHoJhqNw73Ww+TbL0sE039mA4fFJ+Oa/wsg43LIBPvahM1OL6RLEixJNLhXhPdjxeKXQ0QGf/CQsOPeIT5v3EF4JNs7SuEo7RWhhCywbm7fDfDOVdvJiq2SO0iDU8niHi10s5KnCDdwbfZi4FgKvydLQAEvUISRjlBV8DoUfYuCgwNOozEPk0ireu7vh4EHLebyp6eL24fOJ/PoJhUI2C2QBgWrVxfPPq5yr0y/ih7YITKagMQhz2uDeLRBNwEd6QS8J1Nc52batlr/7xgzxhIEo6UiSjig68HgEdB1qa2WW9qocKZx5jKoJAxOWuOqqh2Ovw5GXDbbGTHxeSP2ByL/8F+EdCSuw0oj/6f+BoREQXfA3/waPvgLf+itoOaXBrtYDtR4Jq6Ds0pme1ti6tUggIHL99S4cjvdHRMzphOuvv9yrsLlQchpsjV/uVVxd2ALLxubtEARQt0Dlp2CYIM2BwhDDwW6OyWV2pFcz/lo7h4K9LPYdoFoRiaadxDwO6kM11OCjQitV+pGIIJywLsgQZZw+VFy0sgj1HdgXfPzjVgrG77ciWRfD67tN0qkyEEcUdQxTAF3D6QyhaVZd1lmPvQ5+shNG4pb2DHngTz8ILeGT23hqAuyOu5jsz3Hw9Rxu0WBmSkfTIBiU+MY3QvQ2SWwfhfESNDos+TpeAMkEMWtFtpKTcPx1k2rUoFoWSBTgJ/cbfO1ekQUd7yy6tG077HoDRBWyaajxW4ap9z8M/+NLF/cYvh2aZvK972WoVEyKRZNq1eSOO95D7aQ2NqdipwhnFVtg2dhcCMoSkNqBIgi1IP49TsMgTg2BfAYdlb5gN7cGHsev5yhJCqO+AFFnnlj5V7SL82lVVqNSj4BChSKHeRkZhQxRKhRZwIXn+VT10lMvhm7idkE5b2KaIJgCJgatrQalkoHXe/ZIS9ADf3wTxDKgGVDnP3OGX74IgRqVpVtqmLssyJxgCblUxuuVuO46F83NVmX+7zXBozNwKA9jMZiZhh43RAVL18Zi1jQeTQNRAkGEcslkKgoLOs5yTgaMjVtfm5ve2gBw8CDUBmEyA+mUJQw9rhOu9L8jqlWTbNagpUUmmTSIxWyrbJv3Jl4JNr7DWs5zYacILWyBZWNzoYgB4ET7e82ddMd+TKdocrjSSGvtMOtbX+SoOAdZ01CVCmEhzvHxel43XASUPm4M1bHBYw1/rlDExMCJBx0HOX6HV/lzsHSxyIqVbra+6KJSzAIyiqLQ2qpx//1JvvCF0DlFFljpwnPR0QBt9TAyDXURkY/f4SboPXNisk+GTzZBogzfGIFVTZbz/dEY9NRCVIcxF5g1AvmoFUCsaxdY2nNm9ErX4ScPw/6DsG+/ydiwxu99QuRrX5OQJFi+DP79B1BKgSJB2oS4Dv/XCV2raTAwYEXlurpOirOBgSr9/VWCQZHlyx3vaM6hyyWyaZOL558v4nAIbNp0EVOjbWzeBXIabI1e7lVcXdgCy8bmYvAuRlVq+FJpiHI4xmvCEXRTJlkJ0uydpI4ZRvPz2e1YTD4fgrKDVGKYZZ4ZAjTgwo+HABlimJi00fuun0I4LPCv3/LyX/68lu3bVarVMtdv1FiyRGFoqMrwcJVFiy7OENShwufvgFQO/G6rKP58OEVQrEZDDAOcCmxZBTsOgLJaYOCYSKoWXF741t9AzVlsngaHLHFlGiavbM+h6Qb/8I+wfLmHm2+WWbAAeuYDIqTy4GmBL94Dd260xNkDD0Bfn7WvOXPgM5+BI0fKPPBAHqdToFQy6e/XuPdeD4Jw4SLr5ps9rFnjRFWFK2r8js37EDvAOqvYAsvG5mJxNON2NPNVSeef+15lz3GV+nlDuHwVgpUQj2cWMjjUSTXpYuZQDbvpYv4Ho/zRigYkUWYBG8gQRULBT+SynEJXl8gPvh/ge9+L8sgjSbq7m9F1E8MAl+vSOuhkCSIX6HfpVqBZhR9tsyJI6+bC7dfD6jbYNQ+GxwUaArBxKdSdI41R1axUZTJlIAgGsiwBOpOTGiBTKsHcuXDDRhibgO4u+IPbrftOTcHRo9B5wqpscNDq0Ny+vYLDITEzozM5maK/HzZtaqGx8Z0NTfb7L80ZPhbTeeaZErmcyYoVKitW2EObbWYXrwwb32HjyLmwU4QWtsCysbkEcjn4t+9LZFLLCYgj7Huqja2Jm8mngxS7FIoekfTusDVfRocv7QjwrS9N8tCHVBYINdRwkS2As4jHo/CVryzgzjsrPPRQhokJjZtuctPZefaw01QB9sZBlWB1LXgvwuj0dGJZGJuE2+dbP6dycGwaFjZDUw2w+u330doMgQCUyiI1EZF0UmPVcli/3nqbq6+DFUtg935wu+DWTSfv+2ZBv2XAaok8Wba+f/llkcnJGMViBZ9P5/XX09x11yxdibAidiOjoMhvbVgwTev4hYLBd76Tp1w2cbkEfvKTArIMS5bYIstm9shVYev05V7F1YUtsGxsLoGDfZBIwdw2J7XpHh55UaeoVfAEy5RSTnKCAl2C5XogAjno+4mHf9pwnK9GTJZcpsjV2ejoUPnzPw9jmpxzbt1MEe4/ZLmnaybsicEXF4LzEt9JMkWrazDisX4ulCB1FguH8+H1wh99Bl7fI3DdOg+hgE5Pt0hTkxU9EkW4+y64+XpwOd86JLq+3rIU2LrV+nn9esv+YuVKJw88UAQMKhWTYFDC7b74NF+lYvLLX1bo69NZtEjmrrsUnn5OsExTBfjQnVat2PPb4aVXIRSEDasMcjmT1lbrPHQdDhyo2gLLZvaxU4Szii2wbGwuAU0/2UGXSIJDkhBllbraIkOjArgsV/PfGrm7QJt2MhJPMBkOskR47wgssGbZna+8qC8FugmtJ5wGhrMwUYCuS5xhVue36q6m06DKVjF7W/jc22s6jKWt7yNO2HcY/F7onQc33wAnLEzPuJ8gQPAcacvbboO1a63vgycMF5cskbn7bhe7dzcwM5Pi4x93sH79xbsx7typsWuXTkODwBNP6LjdsHufTDKp8/oug+991yRfERFkiY3XCSxbLvLUVhFdB103kSSBfN4kGLTObXgEkimojVhdkzaXhmly4gPG5V7Ju49Xho11s7MvO0VoYQssG5tLYN4cePoFmI7C5CQUYqA4JRKTASTRABVwAG+W4GiAqqM4c9RVHwfpYyC9t0TW+XBIljUDWCLI4PyDmS8UrxM+fwM8exDKGnx4JbSco9ZqJAk/2gW5siWYJiZAnoKwCl/+FDic8ORzkM9AZztcsxp8vgtbR/A07aQoAl/6kszUlIdAwIvff6b6LOmwLw3DBWh0wrKgdbE6G5mMiSzDzp0SBw4YvPyyTrCmzNC4wlj0hA+FYaWTH38a+o/Dhz4sctstDp55poQgCDQ0iGzc6GDnq/DL31j2FaYBn/goLHn3eyUuif37szzySJTNm8OsXn15B1QPDcGPfgSVCmzZAitWXNblvOvkqrB16nKv4urCFlg2NpdAJAx//Gl46RWYHoXNm6wC6vYlEmKLxP7xCnunZUtomUAeIiunKAWddGV/jimMIPj+CJSuy30qF8TSMLwRg10JK0V4QwM0e87czjShooHjHdRn1Qfgk9eef5tSFf7jNSva1X5CgCWjsLcE6x2wbxD+77+FkWFwS7CuF/Yfgi9+FtwX6ZCgKAKtrWcP65V1+O4QjBUtUbU3DTsS8IVO8J3l3Fetknn2WZ29e03icYNQSGJ0TCSRLoAnAKrw29eJUYKxSQNJF9m0ycnSpSqVikk4LCJJAo8/DS3Nlp1EoQCPPXWmwBofN3nhBR1FgRtvlIhE3lujf44cKTA5Webw4fxlF1g//amVNvb74Wc/g54eK+38vsJOEc4qtsCysblEGhvgni1ww7Xw5JMGHR3QNl/k/uch0lpm79ECvOEGAxxr0hTudTGqNeCSw2D4oLz9ihFYsgjukFWz7xTgsAhHCzDvFJE1k4bvvwDTKehtg3vXg3OWyoUGE1CsMGd/pwAAIABJREFUQt0pEanFc0GT4JYl8MiTMDYMNUEoajA4Cu0tcPQ4LFs8O2s4lcNZGC1C5ynnP1KAV5Nw01nSLQ0NIps2qTz2mI7bLSEIIrmciGHIVqSzcmJDtwG5Im6nTFVzAVBTczJUaBhWBPHNdK4oWr87lUzG5Nvf1pBlq25raEjjT/5ERlXfOyLr1lvDNDaqLFx4+ZVMqQTHRmDXHo3J0TLbXqpy/7946O2dhS6OKwCvDBtnZzqUnSI8gS2wbGwuEU0zOHo0w9at04yO5jh8GII7VByBeo6kAwSuTVC5r4roNDFNEUGQ6NZHQVqOYOZAvHJShIMlOFyEpSdSaXkdfhGHr58iML7/IjzTB5NZ+MVBK/13zzWzc/yqbgV4TkUUIRyyoole1UqZFavW6B3HCWGnabNz/NMZL4LrNAcGvwKj5yjQN02TAwfg9tsFXnxRJxAwUFw6Q6OSteA395WNoUgFzIpEwFvP6bMhRRFuuh6eeMayw9B1+MiWtx4rHjfRNGhstATV6KhJOv3OhmP/rgkEZK67LnS5lwFAZzf8/HEYGa6gGQZv7Kty331p9u69cv4/L4VcFbZOXO5VXF3YAsvG5hIoFDQeeOA4g4NZgkGV9nbrk3g+r1EaHCVcmmKB0co+J9BcQZR1AqbBPXk3iqqCugycN8z6ukolk+efrxKPm6xZI9HTMzv/6hXD6vZ7E5cIiepJSwGA3SNWndZkHkoa/HLP7AmstpB1nKpuubFXTDhQhWEBlhlw813w8CFIJqCuCI52OBKCJ3Iw0g+r66HFy3kL+d8JzS546bQBuZkqrDxHHbxhQKFgMm+eiN8v0d+vUVcn4fHJaD7oGwJBA1Wv4ok4kOQq69acPW9zwwZoboR4EuprobPjrX8PhwVkGaJRE1236tAClzcL957G44NlSyE2bZLKGEgyJJPmW17bVz12inBWsQWWjc1FYhgmDz44wNhYns7Ot1ZRezwynZ1egskyIWOY+/wdTKQjKC6JXg/c2tKIoNx45k77t8HgDgg2w5IPgcMKDWnlMlqxiOrzIUpvb1r5q19V2LNHx++Hw4d1vvxlkaamS69Gb1Qtx/WUBh4Rxiuw/DTBMr8ZhveDT7SK4G9ceGH7LulwKA1VE+b5IHiWtGLQBXcsgF8fAkmE3UDShM4I/J9JEHTw3A3RKAyUIJqEiABPj8JYGV6dguV18JE5lkC7VBb4oNUFQ3krxVLQIaDAmnMEZSRJYMECgf5+aGiQ8QZk9IrJ0mVw+xaRZ14U6B+AsZEwqWicOz/gZv01rnMev3sudJ/jb36/wOc/L7+lBuu9lB58r7FqGWzdAQ3NDqq6iEsw+OxnlPeNuPIqsHGWOlHtFKGFLbBsbC6SwcEsAwNZOjrOXT9SblJ5rbtIn3OSa4MZPuVppsl78squYzJKBh2D9kQK+dBj4G+EqUPg8MKSLcT7+znw4IPolQrexkaWfvrTON6mLe74cYOmJgFVFchmDWIxY1YEVkiBzzbAL+OQ1GC1Dz5wWrffV26xIleTaVjZAZ9a//b7rejwvQGrfkkEPDJ8sRtC6lu3eWEahqvQMwfcOgzmYYkP9hSsmrB9WUgWTUonymbKCMQScGQY3miFzQutQvltR6FFggWNsHnRW4vxswX49Q4Yj8KcZvjA2nPXkDkk+GwH7M9YIqvJaaVPz9VFCHDHHSI/+IHB01tNJuPQ2QJ//99FWloEli207D6qmptIjZsL0NLnpblZ4L777Lf5C6GtBf7fr8GhO2RSCZGWJgfLlr1//BpyFdg6erlXcXVh/+fZ2FwkL788g/c8V9IZTeF7bR6mhQaqz8ps1arsCFb5i5umWNis4aWFg8TZxRQCEBOKrEUAxQmqB0oZDE3jwEMPofp8qB4P6dFRhp57jnlbtpzzuACLF0ts21bF6RSQJC5KXJV0eCMJQQUWnpJaanPCV5vPfb+wF/72bstuwXWBxe0TJasTr/NNf608HMnANSfKX0wTfjIEh9NQo0JGsz5xtwRMBvQKZVVjyqgi+PJ4JQEh56WYcUNEBF2AvEBuHJ72w0oPVA1oroedx619b1lmFeWXq/Cr7RBNWWN+Xu+zRvB8bNM5l45TgtUh63Y2ikWDwUENh0Ogq0smGBT4yldEkiKEp2HRfGhpORkmqXlvlCS9L4mEYeO1cDYPtfcFxttvYnPh2ALLxuYiGR3NEwicW0Hs0t3MeDz4huJ8oOdJvJ4sh/rW8U/bAvzXT7xMmGUkqMeNgoxI3KdCTRskhkFSMeauJ1+dQqsW8HqsljSHz0cxmXzbtW3erFBbK5BOm/T2ykQi7/yC8fQUbItaNVdfmAsdZ7FjOBeieOHiCkASTpo8CoLVIaeckppJVuBIGto91t/9KhzN6VTDMwybGaqeDFLJRB4OILkkXM4CGS1AOhmydqRKIFujjcaA7iBENegKwasDMDJhCSxNh5cPwJpuaFagpQ4ODp9/7ZWKtSblRBRsZqbEsWM5qlWTSMTBM88YTE8bmCZs2uTkttvcSJLAl+6DQwOwaM759z8wUGF83GD9esc5HfZtbC4VrwIbW2ZnX3aK0MIWWDY2vyOmFIViSWGZtx+vI0uyFGL53BfZdfCTOAhRYJxF9DLDEBU02uQaHrvmJhzlAss0HxnzaYrT+wjc2k+57whGtJdSokznzTe/7bFlWWDt2ktrL3/zw+zpXXu/C5pdsCwEu5OWoGtxw6KzFGSfWg8zQp4eRwWPWGRaFynpCsWgh3LSgTEpoUpl3E0pCkcD1h2dIrpgFeVXNKsYfboKR0bB3watERhPQqoKP98DXbUwNwyLzlOXMjIG3/uRNbfwM/caPPH4FHv2ZPD7dWRZIBo16etzsHlzDV6vzEsvlbn1VheCIFBXc+7B1aeydWuZI0eqLF6sEAzOQuGYjc1ZyFVg69t8mLB5Z9gCy8bmImlt9TA6micScZ7170lRwTwiEe+oQRY0IlKckXgbPd1HqJClgQ0EcXM3C5ghz3c5xoikURFTxHb+M8v7Xqf++BiVsIvYpg4yPTF6/X9Mw5Kl78r53VpvuaMHlPNHr3R0phhDQqKeZgQuLMqiYTBDlgo6TkFmfb2XnCRSNmBTDbhOeXcKqjAvAH1pqHFAomqgOWP4vLsJGioz6QZS5TACBpURJ5ogUUo70RKq5Y5eB4gmiAJpGbYXYKYAkbJVL+V0wP4x6I9Cax0MT8PYie7AOW1QrJw9InfwMBg6xNPwJ1/LMzok4/E04HIZrFuXp6tL4/BhnedfjNNwY5h8i8TPBk1WeaEjIlzQSJYPf9hDOm3Y4srmd4+dIpxVbIFlY3ORXHNNHYcPHyNyDpucUFkDSaQvuYQqHlxGnuPODr4RkJmDgIJVcCQhcIwsw2iUqFD/o4cx9X04h6d5OnUD6ozOInkM9ydN/O0d79r5uWS47gI8kyYZ5TD7ERFQcBDm7e80TJLXGaF6oi+8oMMbBQmf2YHHCPCDafj9eph/QtiJAny8A16YglejIKExr+kAkqLiqRrUmQnGKy1URx0YuoSWlSChWMadKpa/lG7dNAMMFSa9kMmCWYTsABg5aPRZxwp6IZaBO1bBTAZ29J+9G3LJInh9D4wM6hw/WsHQXSSTlmpKpSXWXZ+nba7B826RvXUiLW0Ovhmv0tkvsdaUWdsJrx21bCfW9cC6+WdaAgQCIoHA+7QmyOZdw6vCxrbZ2ZedIrSwBZaNzUXS2emlq8vL6GiB5uYz57B8QEyys+SHqsiANAdEE6Us8IdLJE5P3knImICo64T8kxAOUjo4SUjKkDc9VHM6jlyFIhPIgMp7x9BIREQABATECygOniDNdgYI4iKAZUFwuATVqsaYOYGcVtE0Fw9oOp+fE6cgZfHhoUGK0OGWeb4MkijS1z+X+Yv7cckmQbdATSFGMe+l6s4zM9LyW0GFeuIrAhhg6qAbUHVBWgapDINJ8ArQfELcmKYVsXLI0BCAl4/BpgUnxc/0DPz8VxCNwdwuGOrTKBYksllIlgWq9TLHq04O7nAiFnUy3SK+IQ1Hu0RlxuQ1TWfkRYkHRYEPrbNG/zzyilXHtWru7D9H75SKDv0JiBas+rhmH7QH3+qBZnN1kSvD1sHLvYqrC1tg2dhcJJIkcu+9XfzwhwMMDmapqXH8tui9UNAoR3N83mmwLd/BmCKztBZ+fEuWuKJRix/HKf9+iwnSgY9hyaSytocYY0ytaaF791Gmgy14VrtINjqJsZ04As1sxsN5WvnenJtyITmoC8TEtETgaSnARlpQcSAiESL8tvvYyzheHKinnP94RmXPcA2yoOOQcuhlkV2FLK9Uo6zqHKPeaSJTT7XSTcFrcIPnSdZo+3Dky2wNLGGuEuOu4EOMzA2TMXwcMJfx9IHN6KIMmDhdJQxNoqI5Lcd0DZI5a/SPUwBdg7xoGakqAiTzsLDVevgcIhTTliiTJcsV/gcPWV/ra2HPPhifFiiVRdJ+kUpERZctyZmaVCCnQFAnOSqxzQFCFcQgTB4EQYHeJlizECI+a5biqQJrdBR+9Sjk87B2LWy4blaf0rPy2gQ8fsyasyhbmhTdgBoX3L0AOq+gLsdUSmdwsEJvrxNFsdXheTGxjUZnGVtg2dhcAh6Pwuc+101fX4qtW6cZHs5ZXW5+lTvvbGXx4hBerxWv6mOSHYwgIhDAzY0sQD4xG8WPwpeYQx85aPsKMzzIyH31ODbqLDoOiTYnfv88HNRTJUuCPWcKLNOEUhGOHYTnfm2JrOtuodi9GK1UwlVTg+xwvONzLFBlH1H6SWFg0kGApdQSwNqXgEiE+gvaV5YyKUrUctI7bDIn0zfhQ1V0VKmK4RrHEciyyB3DMCSeGu6gWC/jFMrIymEcjRViEvRoDXxQOcyfpn9CWihQMrJEpDDbi2tYUHeQmZZ63hhcRdvcIUKROIIBE0PNzIyfeNw0EFTw1kK1APkCjKYhKEN7LXQ3WpvlyxB0W+IKIJe3PLKKChyIg18Ct19ED4gQUtBzWKGePFa3v6HjrORxry+gd0A6FkEfkMiVBCQdXngVlnVbdV6+UzxFMxn47vfA6QKXEx573BpYvXrVieelYA0oLpfhYx+D0CwIn20j8OujVsTKedrVIVOGb++Gzy23ui+vBHbsyPPLX2b42tdq6el556/99xNeB2zsmJ192SlCC1tg2dhcIooi0ttbQ29vDZpmYBgmiiIinFZMM0iMAG4cyMTJkaVEiJPV4yEU1hECQsBfWL9sBZoNqpM/Jx0bhoZ6DDQkrEhZmRJZbZry2E8J7X8UYThGZdsUcaOTwIowx/7uAYbTEdzdKwi2t7P8s5/F22BNdC0mEqSGhlDcbsI9PQhnCY3EyPGT7OtoyQRdZQWxuYPp6ihPuhLcbkTwqnUgXvjbiI5xRgn8rkkPYZdBRMkjeF+kw3WAJmkCBJ1JvZ5a5xhJM8SUXI9ZlcCUGJI7cbhr2KvladBfAlcdEq1oIqxx7+bx+E001ozjSc6nJhIjn/Mh6CaN7RPEo3XoogJuy/ndFK15iboTat2wvhFcrpPWEdNpuH0ZPDMIO07YObwwCLUeCAdh/wgEmmWqFZXKmGDt0MQa3qyB2K5TrToo5E2CqRRaNkd+bwgEE70KUymBvcehswluOGUgdTRqeXA1nhi7EwrC8eMnBVZ/Pxw6ZEW09u+HjRvPfLwHBirs3VtCkgRWrnTS3HzuztJ4AZ7ohzb/2V3u/Q5LN/70IPzZtVb0773O2rVuwmGJjo73x8DmSyFXhq0Dl3sVVxe2wLKxmUXk81x1avFynCgKEjISbi7QKGrnEwT37UQyB0lvTCItWkLEWMn0+G94Of0LYo0a9YFRBjfOxS01UXdPA+EDo2QO93Owp5GBlnbU8SG83/4N+378Y3o/9jF6PnQX+x75EZVCDslQmbvhFnruvPMth93BCP9SfQXNlcbtKtCfMPnod35El+SmXK+Rqc/j7Pw0cvPZTU9jGoxUdRySQbcqoSLiQUVEQMdAQiRbEZnMyciiRlPoOZb7n6RqyhRNGZ+QZ4HzEIscB9ldWI7iLHJAXEp9McWkoDJu+tkvNjLP2QhKBY9QZE6Nn0S0SqsvRqtngqWNezFD8GT5A+REH4IMKIDbairUVFDK0OoFnwpZCZ4AZA3CGrRlYXkb9JegfwqavJYfl7sXJg6DGYPGDhhwQnOvjFbVSORNzPIJGamBYUpQlNC3qyT2q0gNhvXOWzChAqUsuAWBr9wJ/lNK+Xw+KwhZqYCqWgX5pw5qbmy0ZgtWq9DRcebjf/BgiQceyODxCBgGvPZakT/8wxBtbWcXG7unLFF5vhFCXhWGUzCQhJ7zZ4PfE9TUyKxZY1/mLgg7RTjr2K88G5t3iaW04USlRJU51OE4o9T9HAzsRwp3ECjX4R30Iy66Bw78mokjf0+jUqCzkufbPZ9moXqU+lwcZIH48g4KikrvnCxj3WHy97yCPjpBThvnlfg0B3c9gmOuH31FI6biIXr0CbqqtyIrlujrJ8W/G3tRhXGc6AiCgCOc5VCvD6OSw12XYdIoMRh8BRfz6DLbcZpOnCeqoL+f1Lg/X2SGMqousNpf5m+DYYyYg2K2kYlQlHDAhVYRGEw5CHhm2OR4iVi+hkb3JItcxxAFA82USJsB1np2oogVBqUuZpxB1GqRUcFPl6CwR17MUnMSw5xCcc8Q9rhZ4hvj4Ew3iWwTTZ6jfLDj1zwZ3cL0aAtOSSHggQ4nDFahzQUBGTrrwAiCo2SlC3NuuHY+LK+Bf90LHSf6CkYSkFOh2gsJExK61aRYTIuYAQW1bFIucbKDsSJZFy9ToJJxWb93YqUPK0ARXuqDV/vg5uXWMSYn4dHfWF937YKuubB2Nay/9uTLorYW/uzPLBHmOsu4wiefzFNbK+H1WqI/Htd57rk8n/nM2SdRH45B6OyOI2/BIcPxxJUhsGYTTYNSCTyeq3P4s9cBG7tmZ192itDCFlg2Nu8SMhKLzleYfi4WrIbXnkEQQF6zGZDIj+0kXiPiq9MZDHZSUZy4jBJKuUxZtCYyV9tDOJ/p5+bd3+WpA5oVTQGMUIWikOC4r47s/A7UcgVXk8SL0lFuZBECAluZJKunmZHmYZrgECq0mKPMXX4cuS9GKS8Sb5mPx+Ngq9bHjlKSXL6HGxQHN0ov8U/pXsYqHopHPZhlg2mXjNw7QvjVOZhGhBdxUr8xRcAlkq0IXNv1CgExQZcyQ6cyQNbwUBTdSKZB2IyREgK0icN0SYNs0zbg0sqUNZnx4CoCFFijDVEWPATwEKr7IxLJF/DSTIMTqql5hLUh9L4eGvUAf7kOxBAMFKBZhtGUpX96gnAEaK+zUmFDJagPQ6ps1cWPaDCpw5gL3BpIJatQvmRahfJGAQqaYN3ZqnG3IgLCKeZCOqCLVn1WGiiDt0mgtgae3ws3LYNi0aq9EkRYtxYSCUim4PbN4DxNAJ2vpK5QMAiFpFO2FSgUzm10pOkX1iUoCKC9G+6z7yFmZuD7D0A6A91z4JMft6KKVxO5Mmztv9yruLqwBZaNzXudlTdC+3yQZAhZI3Om3TV4YnkG29vZ5roOwxDRkBAEkAQTQa+il02qiolkaoi6Ja6aFsKaTxXoO+wg2lqH6+Aw+F3UrPTSJ47RSyf1eDhKjmmpFtUooKNQwMmI3sZcc4gZqYLcCFpTmD2azq+KftKmjCaN8o+DnbwWbiRrmrSmjrKgsQ+Xo0C+6CZVDhHurhIu1eIZ8RKZCpKZm8XhrBJRRpgjDOBV05iAz8hRNRTKggMnVQLVJAmphogQQzdlUDTIahTxUpWWEZWjVBBZLnwKVahnWcM+uoJphpIB4qUUhhnmz1f6uakDAqeJlIpudQ96ZPhlFHZkrFSZW4QeN2SBAazh1oIORi3EHaAdBp8CctUyKi0ogAmSC8hwMuUinEgbvylKREAAlywghSBQBxEPuByWeBkYsArY29utzcNhS3QdPAR1dRf+slmyxMHOnUXa2hSrlmxa4447zj2YvN4LgylwvU1gtaxZtWrvJ5561momaG+Fw31w+AgsXXK5VzXL2CnCWccWWDY2VwKRt85rySy4ib17DzPob8HvylA0RFIFN7IzTFDLUS2K6C+O0HKjSa5fgnoZI63RvU7HETDxr3QSCEkIhkllcwsOM4uETKFSIJfIIvkqJKUQ5UoduilTqqo0TUzjPm5wQFpKIBxh2BB5Q29lggYqKMgOnUqwykyywsfbvosoVCjpLmR3hQVqDJ9cRs3XMlC6nha9g5CvlT921rO7VkDOpJBkHdFpoAkKsl7BRYGy7KCKilfIomVMDK+IaFTJl0DWKpimzobhXbhw4m//EA7RKuB381FM50/pbRxDwIuHjyKdw2FelawbwAdrYZ4HioaVQqxRwO2FlApGBZIeKEeg7ANvCtQotNaCQ4IBA5xRyDtA9IGRAQpYNV8SlsJVAB3q26C1HUpY3X/xNHzkRPrPNM8ynkg46bxxoWze7KNSgd27SwgC3HCDh/Xrz62M1jbDgSjntYnVDUsELroAA9qrCV0/aY8hYD1HVxteJ2ycJQ82O0VoYQssG5srkJ7wfH7MjYwVNYLOGAE9w1S6kcxBCMZM8run+MTnJxDqHezRlhD8Cx3X9iweRz2R6gSxoQJmExRFBdf2YZzXdaFmnAx853sMJrJI7S1MrbuDUt6JIEOwnCBcSdEXXs3qmWfxbdvF4LrVFBs7qZgqClUkU6MtOMzdxR9gHEtjNjup9esExCwJo4aSs4VaytR7XqSh9jE6xCFGS24cDS4SmRB9g930+vehdpUQMdEFSxKJgo6OQKwUYFBtRcsalCsOpKCEkJphzswrmEKVlsiN4LceH4lGfHwZkyICbgQubMyMKMCC08YCmUBvLezNWXMKQwLEnNC6GOaOQESG7jo4kIRUBPbuttKI2eoJkZXHElYu6+LsroPNS6EjBKmStf+PXA8LT0SsOjutVGAyZXUO5nKWsOmca6Xx3rSLePOif656IFUV+OhH/WzZ4rPSy/L583+dIegKwmjGsmk4HcOE4TRs6gTf+8zx4JYbrRThyCh0dcL8eZd7RbNPrgRb+y73Kq4ubIFlY3MF4pbdfCi8mX/+6TH6N3XhDmaRS0EWxCNM7x/jjo99k+ZWP6VMiVUk+MCHr8W440uI7ibcY4epun7CTE2a8vwu5Lwft3orXTvG0eIZnB0dvNy4EmPCiThfY56yn55APx7ymLrB0LSf6ZlF6JMSkfgM3flDNIfGUf05uiIj6F4JGmQkR4Gi7kVHIqLEeZHFPKq00mEe5xbzOWYkDwYiolym03WUH9V/nMVjC/iD+HcxakQKuJCFCh4KDEdDjHi6eLV4DWkxjOkAwTQ5qlX5D18nHh18hWk+7JFoKbWBYeLw+RA4i1J4p4+1BL1eOGLCPMF602w24XYfbOm2Ov9qg/DsUXjaCZ0N8JujcLwIuQSYR4EIhMLQ1AC9Syr83jVx5gQEjFQYt6TQeMq4pUkTnDdC4mXIjEAwACtvgL99ArwSfPUWmMzAU2+Axwn3bbKiaOfiQg02RQHu7YUH91upQr/DupkmxItQ1OCaVri581IezSuTpib4z//JMnwNBEC6GsdCmtizCGcZW2DZ2FyhXLMygmxIPPZ0lJTZgFo1UWWBOz+5ghXLvgraz3DJTsI1d4PjWstVE6CxiebVN3E3BrejUUTHiUxMyNNvGGRUB0PU4Qsn+Er0b8kuraciKuRxM6C18mzjrVTDEuuf3M4dBx7FuFtib8cyRE+E+dIIBVXGbZRJGDVUFBcGChNiI5NEGDPquEbYTlyO4KKIjI6OSFYKEibGtvYNtI0Ns0zejYsKmibSF2/lhdJ1/Fq+j4wRBAlkRcOsQDiWYPK1COszT9BbuZ8D4yqH9QW4QkvxNzfTc9ddBFpbL/mxvqcWEgb8IgcuCbodMPoq/EMUIhJ84U64fg6UNHjoOIjzoSML8WGrZgkTfItA6M3xuivJjoEKS8J9rFMCfKH+GjhlxNDDE6DVgHYrfDAIe2dM/vo/YOqIgFGCnU9Azwq4ZjHkivDwS/AnH7buWy6bZLPg91sRrHeKR4U/WG51CW4fhYmsFSFbWAtrmqHVf3V20F0IDsf5mwqudLxO2NgzO/uyU4QWtsCysbmCWb06xLJlAaanywDU1ztQFBG4FdRbz3tfARE3Km9W5SgrVzKzfz/jw8PoEZFPJf4JrVsiLkRIEmSo0sZouZ2KqdJ4fILFuw6Q/GiYR9s+gkfKsd54CZdQRnZWMZEQygKmJFA1ZVJCgHLZiaaqSLpBWVAwEfEaOUTBoGiqSIKJLsrsDK4hSQ1FQ2W6WMv+0mKO5peSKfgwVAFDVqhkVeao/SyO7ybYGWdisJbif0tSShYRAyN03w6xgIeJf/3/uPmPPomned6J6vMzMU2TF14Y5vDhKNdd18qSJVYdF1oMigfASOOWavjTukXcUONhd97k0ITMs1GZWi+k8zAWheYIzGmFgVHLm0pNQHE/JCtABFJlDX+uSDUWpCippNMh+qt59r6m8Z9XqDhEmN8M87zwzIyVjvv6XoPdWY38qIDpkJArcPCYyEACqklwOKGr21ruxITJ979vUCxatg2f+YxIU9OZaiiTMdm/3yCfh54egY6Ot3q3ySLMi1i3eFxDUQT8/qsxZGNzKrkSbD1yuVdxdWELLBubKxxFEWlpObt4eCeoHg8rv/AFejMZ7khWaT3SR2kkROPMFEmfh6lgA6ZbRDdk5h45hlLJ8UTgY2QfF2iYF0NcomMKAn4hh6kJpAlQNN3oSPjIIAoGBcHDPmkJt+lPkZW8VMQgslDFNASGxVZM08SMF3k9sRDx0CTlwylGm2pI+CJWoXdBxDBl3C1RGuYPMtTdRVlfQNPAC9xTKVEwDfY4l/GPbX9NtqUNn7vI6gPb+JH+IGLtH5KgjrJTRdcGGSnFiRVrESYCvP5YPy2+3K31AAAgAElEQVR1Lh766X5CzWP48z/Hn9uDIPnA3c6UrPJ/ov1siy2kookMJ5rAITMZC9DgUmluFEiVBO7eZnI8p2M6TRxDOtkZFeosAaOrJvm8F8FhggzpSS/5AS9jgwpP/QBWL4I/uQe2rIR0GcpRSDkNpGoFU3JBEbQMSC6T3KRA/zC4HTDvRP/Dww8bSBK0tgokEiYPP2zw1a++VRjF4yb3369RLJrIssDzz8MNN+g0NgqoqkBbGwwPl6ivV9m7t8Qzz2Qol0UWLAhSrcoEgwKrVkl0dYF4mqfD2FiJI0fytLQ4mD//3N2KpzM2VkJVRerqrkzfA00zGRuzRG19/RUc3rO7CGcdW2DZ2Nj8FlGScIVCfN13lG17VVa/8DIPbvkMU5VmDEEFA0wENFNE0DXG36hBeL3IyP4Iq3urBJQkA6VOOrIjhINxqqaKaJjMl6KYgsBooY1dniVEzCjLtL0ook4GH9vEa0hTg0fL0P217xJ+fgTN0En6BDq2jLK79jZeb7iLiuok2DrN+rXPoStuqqaJgk7Lkf0U8jr9coR//evHaVyWYl7bbhJTDUxKc/m72j7Wjn6eh6Kf4EDLcrxqjpXqYRa50gwJNbwcFelKRfG5xzmeGCDtrUGLdNFaKhMT0/zD8XsZHOukVFbJa24qgoIggRrKM+YrcN2zfnJpgbhLQHZpaH0SlZgCZayLVhnQJLSSgSpomDKQkNHjQFZAS8GBvVDaYtVC1TpODJouitT25Mm9IkJVRjdk5LyVprzzGqsG682OtkTipNO7zwex2JnP7wsv6Bw9KlAsijgcUF+v87/+l8aaNSAIJsViGkUpUFurkM8rpFJOXnpJ5he/0DFNAV2HpSsMPninxKc/Jf42DZlIVPn3fx8HTEolk899romeHs+ZCziNctng/vsn8Holvv719kt/Ab/L6LrJD39ocOwYgMlHPiKyatUVMEPoLHidsHGWivftFKGFLbBsbGzOoEYW2Gg4GPE2EDg2Rfz6COWSE4daQhR09vcuZ/WuVwm3Zcgckqifn8Sn5ijgQSs70PIKWZ8XwxSpT40S8GRZJB3gbufP+HX+Tp5Tb+AVaQX16UFcgoYumCzd8UtuH3+EvBpjqlQlZFpNgcZ3HmJJ5DV6m79DbO0cCvddj7+SJisp+MUsKlWK8yMUdTi86jZu7HmRzx/6G2qOZHgltI5X59/MQXEhA10dXOfYxq5j1zLq6mbc0Uq8aRtV1YnjRonndi1g00f283wgREF1knH48XtSHNi1lCPDc8gYAcpVF5rIb1v5SmkHTOjExwRIAvVgthvwmgy6AEUTpgxoBAbAcCqUdBFKIJsgRURED8hZcOgw1gfaGriuFo6kYDIq0nQ0TNuX+0gOS2T/rYduv0DvQqiUrdsHb7aes5Ur4aWXIBQySSTOnE346NPwZ38J+Qy0tYDPa/Lyy1Cp6CxYYLJ0qcpTT5nU1JiEw5DOuXnsKYlyUaCzXSSVEslr8Hq/SPIXJt3dJtdvtARWPF5F00za252MjpaYnKxckMByOERuu60Gj+fKFCXRqDUTsqNDIJ+HrVtNVq263Ku6OHIl2Hrocq/i6sIWWDY2NmegUIunUKVp4TKa+tLkfR7ElI5WVfA4i+RafTz2gS2sPbSDNz57LfX+PAXTRT/dNCqTTKQbIGBS3TWDUYlizlHwNhXANPg914/56PhD5PdlifWZiAGZxKNZFrZUyLkcBFeIxB+HShHEojUzeYH/OEHfcQaJY+7L4vUaVBZEKONiyOxGv7OHsW+20Ls6xRdf+STfX/hpHlz+edLOEKYo4hCL1EvTPNd2Kw39E4w55zBTbOPVqXUsdezBc4tCNTCfnx1bw7qmnSx17aMuOcGh6V4OjC0jRYBKxY1eEiArgobluWAI8JpkiauAddO3OS2DqzHDEleabHVnlYEw4JXBLaDJ4AoDRdBrYEkTTExCPAlpAe6qh9//EFQqMsPOBjxLYMldlhDRNBgaNfG4BRrrreds82aRQMBkeNjk2msFrrnmZLpq1xvwZ38O4xMiugH5flAlg1RCB1PgwQdzTE1pdHX5ufFGH4rTxf/+ZxHFYVlkeAOgKCZGBrKJIscPa3zzmxLLl3nx+0Wamx2EwwojIyUURWTevAt3It2w4eyje64E3G6QZZNk0rLTmD//cq/oErC7CGcdW2DZ2NicgUIQsX4+/qkjdE4k8ZFBUQ0m0824jDxOtczwig6G5nRSOzOB6RVQ0EgLfmrUGfQaFed4Enc8RkJ2Ie6LM66ECQWy6PEq5lQRQ5EI9pioiRztKyuEVeifMnDLIo0RUHVITsO1d0CoF472yxgP96F/+zAFN4Q/HEH8n9ciNejsrVvGgX/5U77x7F/xqdoHOD69FH2nRHGuhC65mDEaiXob6Kg7ind+EW2PGyJlxvNzaWEYr17iltWPMBrtwCFUqHdM0B/t4eU9GzD8MtWKE6METEqWr1UOcAHHgThWqK0NmDrxAKaBSaAoWyajx4FrsYxHD5uWOKsTyAYgqFsDo0enDBb1VjlqmPxqtwNZEPjyOmgJQIMRRjDhTa9UWYZ0IodoyicWYvlcbdggsGEDpFKwf79lJ1BTY/L1v9SZGBcwBMArUIhAQZKgakJGo1AQePbZCqqqsGmTj5/+QsDp1AgFBXJ5gVwBGsImk1MlHLpGe7NIIm7wm98UufdeD263xBe+0MLUVJmaGoVg8ALnbF7h+P0Cv//7Es8+azBnjiVyr2hsgTWr2ALLxsbmrHiXf5HCo3/BXMcgPeNHGWjpxmemKBZ8eEpFRBPSQohYowNXUGSh2MycbVuJjhQYXakx5Q5QXzeJXgSpCuXfjJGYKSLVCFSzFSpJg7pOjYZGE0ctDLwAx0Z1jJ06ahIaItCyCAzVQ5/uZv/PotYoGp+A6JOY/mmcxuY9BP6bF9E0SHYt4p4fPMWR6mpQRTBNpB15Pvv736ataZSfT32Cg0NLWFy72ypcSjgwvBUOFZeyobCVQjHAHS2PEdMj7By8hqcObEaaNqitiWOaIuabwqpy4qZghdcCWCIqDkSxIgExoCjCHN2Kdk1J0AC8YIBPsO67zwBDIOUGfYNB960zbNcq5JMwQ4AuwYMsqowk4IevWc/Jp1ZDW431/fz5bhyOM4uq/3/23jvOsrq+/39+Tru9zJ07vezMbG+zlUVgWTq4iLiimMREBVSMQWNiEkti8o0m8WE0iRp+MdGvX8SCEqKiRKSXXeqyne11yk6vt7dTPr8/ziCLwLLIbGE5z8fjPmbuZ879fD7nzMw9r/uuR47A7be7Vi7bluzYYXGgSxCJSgq2gqgCLQxWCWhTEHtNAn4HRVW56iqdQEDQ2gyBkIIUDsWioLFekstJUpM2ui4J+BRaWmB4+MU7cjCo0tHxFuuhA3R0CDo63vxZlmE/rFkwPXN5MVgunsDy8PB4RZSamYTe9Q30xPe4cf3/8u/v/QC2oVHw53GEggPU60Msilh8UFvM+YNp2PkEM6rOpfORXrbH6int8XPoiXtwlrUhlRjBrn3ITIZgDbStglgIRp6F/Y/BxCBUFAfDAUN3yx/oaQhdYCJ2ppj39gBHFs9lYulcRFuUUDnDxD3bSJSKmIrGnofmkV7cAmFc4aMKbDXM/938Cda+82d8qP7/ceDIXFJbquiz5jJKHVgO1OjIkKAzuZuKDDCRqWXfkQVuUHlB4ABCccDWYCauuOrHtVrlARP3k3966jlADpTzTIg7OAd8EAI1b6J2WCjCoWFJLwNPtlIWOpg62Sdstv68Hmx4MmCx8PKDNC8fIhps4I6NC1CFq9se2g8fPs9dIhR6+U3dceDuu906WNEoDA1JCgWJEAq6Af6puHucqcpbmqB5hklDUmPOnDA33OAWerrwfMhmBbfdLrErDsKRhEKShgaNfL6CbVtEIgorVrzx7FWPM4NcETbsPN27OLs4aQJLCOEHNuC+1WnAT6WU/+eYn98K3CilPPF8Xg8Pj1OKqG7Et+5vuDKfR+29jzvjo/Sp1Vh6iLDusNqo5ia1hgR+CErQ/fgnyuTSUaxhlUTbSirf/wWlvTswfIKhbBlFwKxzdUa3KQxWVLq3FymlJSICTgrsIlR8oDpQiYH60wqBapV9f3Mxk+2NBHf3UfvMAdrXSCb/rJUhX5hHv3U56XCzW2JdYSpGSgASgir37bqOvpnN/Mmcb9OvNHLOnu38c8PnmQzH0Ayb6liKAEWGczUcGZ3FRDkOBYFIOmRLYQLhPNnaGCSB54EqXCHXhGu5snnRuhUADIl2VRnzGcM9ts4hHMtQtH3MOu8AWlWZ3mc6IKODT8CY7s6hgSxp7H5uLtF4hdHyw8R9C9g9AIYGixqO//sqldxswhcaRVsW6DpUxyWpokANgJYGJQxGXKElDFddXgMC/vD3ld8UEdU0uPYdgkvWaDzyiM3ddzsMDAiWLPExe7ZGe7tk0SKNzs63hivwLYNXpmFaOZkWrDJwqZQyJ4TQgSeFEPdJKZ8VQqwE3ryRjR4ebzFEKMTl89/L+VLSJ20coEEoxMQxMSexBLzn4zA5wpFf3kcw7AMhiLTPId93BKtUIFLlJzORY3IsgghEyQ8XiNRFEAlAkeT6RzBNKJchEnCbKTfVQd9lLRRnJ0n+5GlaYjlWrJMMmY0EhwrEgt1kB6rAErAQhN+mpmmQhplHwYCMGWG0u56dg8vZ61vIs855fK7+H/hO7438uf/rFPwhLFXBlDrt2W5+bb+LohqCCdCXFLHSBiJpohgmjuKDIFDEffesBWIOTE4FvleAEFAPaoODqQJ+EHGJFi6jxwWxpgnUOhtFAVtIKAv3tSpujJUAOSowxDD/8YjGtkMmERRm+FTOv9i91PkCBAMvr6ru90N8qn9hqeRw7yM2Ow84KGFJfa1Cc1CQqgjaWxUuXg1/9E4QtkI8BvX1L/+9RyKCdes01q0Dx5E4Dmjam7NelcfxCQdgzeLpmctzEbqcNIElpZS4n+nAjTjQASmEUIGvAe8H3n2y1vfw8Jh+gkIwRxznbSNZD8l6HO1RpJQIIahftozxQABVODj5CTT/CNGqANUzgmQyFram07J6GVY6xOTBX1HJZHAcm7IDNWFXc2Tf1oh1JEdof4rI11oZysP9ytVUVcZJjgwxb91udj2+AvkcLPrDbdQt7ads+zF1jVJWI5JIYWV0Ng+fQ037IP+v+UNcYD7LP8u/oGD4kbZCVSbHM/5zyWbjqJqDbYJScqBKYpkKis/BcRxoVOAgrltQA84RsF9CSUAGN4MwBCigzbIxtzjIWoXKsIaxsEQ4nEMGofnSQ3T9ap4ryjRc64GY+qo6COnw/YevJe0rIh6XBI9Knr1T43N/FeSZXQoXrIR3XvHSy68osG4d3Hor/OAOh1xUQFwB4TBQtGn2Cf7mIwp/8H6VSNC1bp0oiiJQ3uQx3G+EFyrkn63kCrBhx+nexdnFSY3BmhJTW4BZwH9IKTcKIT4F3COlHBTHaWolhLgZuBmgtbX1ZG7Tw8NjmmlYvpxDDzyAEQpRu2gRRiRCJZ3GiETQgkHqFi9meMcOoh09SMsh2XouiqZRSVcY3LaNwugwTjFNugRJP654KRZRNCgqfkKOScjOEbYKpPQ4aV8cOd+GnMKuZ+azc+8KtAaLjsRBmmNdzNeOMKFWoZpl4oUU5/o20hHowgopIKB6ZBIdC3Qbp6zg9xWphHyY2wKEbhqnMBpFSduoho0dkzBTuHFYAQVUAQuAGbjCa78EKTCf9mFcWsRRwd5nkCVJcChDf0s9Lcl+2q/sQk6q9Dw1ExnXYFy48VwCqFV47Km3w14bhotQ0SgVYWu6xD/+g8nMzjBKGRZ0qESjAr/ftTYBzJ4N695t81//10YsVXFsganoYEBfSfLAI4L3XS9PuAm0B/T2wXd+BH/0Hpg3+3Tv5iTiuQinlZMqsKSUNrBUCBEH7hZCrAGuBy4+gdd+B/gOwMqVK+XJ3KeHh8f00rB8Of2bNpHp6yNcX0+io4NSKkVhYoKF730vtYsWMf+663Asi8MPPkj/xo1Ix2HeunUs/+hHefYb36B/w0NkymW6UhD8WTfKB84h6zOov6eb8d+fTfvkIcarEtihPDF1kqQaJjMRo5INQFSSMIaZYRxkYG8L+8pVqC0VVN2heecwasHgaGUGvtphIsuhaXCIwdpaqtOTBJQcpq7jqy5RNn0UHoujXG6itDjEJwbIZJLIgEA02agZh+JwAGFLbEeHNgV9dQk5JLH2+uF58C0qI1dUsG2V0mCIPfedQ3DYprp2lNlLD1JXM8y+pxeRPpwAU7iuQlVAF7B/ykwmVJBQsSJ0d2cJBk1m1il8/esWlqXR0aHw6U8rgODnP4eHHi7goOEoAlmtQFFAQGL1CbqLkn+5FWa1AhLOPw8WLjytfy5nDJWKjWG8PHkgGoHO+ZCoOg2bOkWEA7Cmc3rmei0XoRDiNuAaYERKuWhqLAH8N9AGdAPvk1JOCtcS803gatxCJzdIKbdOveZDwBempv1HKeX3p8ZXALfjRkT+GviUlFK+2hpv/Ixf5TylPDXaRQjxQoD7x3GN6OBWjjkipZx1vNeuXLlSbt68+WRuz8PDY5opZzJ0P/44A1u2IC2LSHMzM6+4gsSsl/+726aJdBw0n5vFZlsWA5s2cffffQAnP8EMNc3uKy7FLhfw7RkgGjKxkkHGLl5I40VhFqZ28melf2eyHKdry3yImgRrcwSqs6hSomoW0tZZN/JLriv/DLUni6MK+hvn8PNZ1xCM5lnu34nA4QeHbyBTqcYa0EkPxigFDZxWFcW0CYgCdfUDqH4HYcPgnmayRgh/2iReHCeYK5Nv85NXo2S2hCFvQFSCPhVrVXGtRkJ3qK4do7pmmBxR4r4Yc8ZjPPQThdwht1ch40DBdEtKTMVmgYImysxdoHLN1ToqNocPq6xZo/D7v69w222Ce+8FNZjjEdWAxNRn6AqQBiUDLUKSsBSuukQQi8JkCq5/L6xccQr+KKbYuxd27YIFC84ccbdnzyTPPDPMjTfOfVmfxdOBEGKLlPKU1YWvblwp1350eu6zd3zp+HufMrbkgB8cI7C+CkxIKb8ihPgcUCWl/KwQ4mrgk7gC61zgm1LKc6fE0mZgJW6S7RZgxZQoew74FPAsrsD6dynlfa+2xrSc9CtwMrMIawBTSpkSQgSAy4F/llLWH3NM7rXElYeHx5sTXzTK3GuvZfY73oG0bVTj1YOj1d8KBlI1jeZzz+WcD36CXevvxNm9iYuPbuDwwkUcvqqTzHiFUKPGFZHDxPZk2DVrEZ/W/5Uva19ghrKP5tFuRjKNlEI6F85/lB2VVfTtn8la5WH0Yg5lex6QNDV0Eeq3eSR9DQdD87li5oPMbt9PT1cHxsIy+ZYI2VSUVKgKVbWpCk9SkX6yTogmugkmMxQtA0d1iMbSsFMlnDcpRCIYiTKVnAETwg3AtwFpo5gVHCNA2orhr84hVYVaVePCZSpzZ5l87Y9UbFuZCnpXQVruRZGA4pawCIcVBoegPin5yEfg4osVHn9cMDkJSy8r8gu1Akf9kHVgQvlN7S6nDL2KwFQF27dBOAyLFsP69ccXWKUSjI9DY+PLA+tfL6Oj8KMfuVXQt22DT30K6ure2JzTQVtbhEBAOyPE1WnjFBUalVJuEEK0/dbwu3jRu/V94HHgs1PjP5iK635WCBEXQjRMHfuQlHICQAjxEPB2IcTjQFRK+czU+A+AdcB9x1njpHAyXYQNwPen4rAU4C4p5a9O4noeHh5nIIqquiXFXydCUTj3/Z8kkFA4uHkT2++vULxzKwH5PGrQQNPLbFoQJ/aBVuY6hxia00xrpYsnwpfRUzcXRXdoLe+l3krxmEwgFMmkGiGeFNi6hhQCtVqQ9xnYATiUmkVud5zLF/8aZ4aCzKjkQwWqgpMknAkygShSF5h5H7HyBNmmEP6mMg32IGrJpJmjtMzoZfcjS8gNRCmGE26RmgDujasAlFUc0wcqmJrB4GgLht+mO+vjJ4dgWb3KqvfC5v8Fx+8gYxJnWAXLtWKphsOKNTqJqE62wSY2X6djhYqmCZJJMKXDSGcPfQ+1YgSLmGN+pDYVgC8AP6h+C2XxGP7FowgB2w8kWaLVcbzbwU9/6oqhj370jbeDKZfdel2x2AvZjm9svukiGNRob4+c7m2cNsIBWLN0eua6A5JCiGPNYd+ZCvs5HnVSykGAqRjt2qnxJuDoMcf1TY0db7zvFcaPt8ZJ4WRmET4PLHuNY7waWB4eHq+Koqro+1J0HXawy0wF4VpYRYtkFNq7x/F/N03qnHpCH8xSHZtALlC4KHY/Nz7wNaz5YdZr70A6KmXD4N/sT/NV/2cI3qTgqAp7rflsDFwAhsBochgdqCHlxEgkJtH7RgiHYLK6lgJhCqkQQ2NNRO0sxaifkuqjOBxA6oKVtTsIiwIhWWD5dZtpPNLLvVvfDXMl5AXsBXpfOCnFLUi6QmAXdNSgwUhKMHkUUvsUup4HK4tbumGVgHka0bKFmpbMWmHwzc8qpCy4b0gl7oc7D8DnV8GSJSAjJt+e0KnuHCF3OIpjaFgpHVJuAH0knKJt5RESq0cYGGmiSdEZWzFO7+wM//7oDCY261x9scqqVS9NF6yrg2jMLV76RmlqchtRb9kCq1dDS8sbn/O3KZXgvvtgzpwzxwV5ppMrwIat0zbd2DS6N1/JpCh/h/FTjlfJ3cPD44ylMDbG07f/BLP4YiUDW0LE5+qUrgz4yhaLwgNU7hOkrotyTeLn/M23P0615vDgJetwhEpQKzARqWafMpePWLexlG2UpI+NoaVEkxkK+QROTidUmyGolyiJAGHDpnXrLuqX1LG1eTVjE3XYZZUKftKFOLpZcVvomAq19igpX5zhVAOq0AnPqhDLZxjfpCF3TGUIvtDwOYDrstsHnCvIHXbLO2gTkDoCHMGNUp2ngKZAs03GL1HSKmqHYM9BeHY/MBuKEsZMuLsHLqyDBTM1gnmVyKo0yphkfMwHOQFJENKkUPFzaH8HerwVGVfYN0Ogx0wO9wl+8U1JqRe++u+Si1dJvvCXgvPPd38PV1wBl1/+xt2D4M6xdq37OFmMjbluz3TaE1ivi9ObRTgshGiYsiw1ACNT433AsTK8GRiYGr/4t8YfnxpvfoXjj7fGScETWB4eHmcsA5s3U8gV0dWp3noSHAk+FSZNKGTdj6sV1WF13TjkFW4euJWkbTJMKy1WL8K2SfjGMWMGw6KOAaeRARrQRIkYE1xp3M8+/2L2qfMw8wZ9xVbi/kmCWgXt8AhJLUcx2UxpbpBUf5S+4Rj+cgnbEihRB+lILGkQzZSYMBsY66thbChBaSCClgIzBfinTuiFPoYmbmPozQKyQB4sFVeEFXEDLFQJvRIUB8IKTtRhHIWH9gqaDKivhkdSsCABOydgzyR8YoHKu+vjdJWepr+2GaeoMNzdDNgoWUmyajflVVFK9TXMb91LsnYScQie/I8LKWwNuGtbcN+vJQ8+AH/yUcFn/8q1Ok2HuDpVNDXBJz8JyeTp3smbh3AQ1iyfnrl+x0Kj9wAfAr4y9fWXx4x/QghxJ26Qe3pKID0AfFkI8UJu55XA56WUE0KIrBDibcBG4IPAra+xxknBE1geHh5nLGMHDhBubsEe6aNi2jhThv6S44YlIV3bf2oCcjmTVLmaYP84ig7Dg0GW5/bSlOlmpLqeTDRKi6+bQimAJTQSvnFy+RgThSSXhB8hG4zQnZtJ1o5QWxok3GwRHxpkRqpAYW4Te+e20NSRZdRqp6yE8GUCBKvSGH6bUm4N88a6eGrnYoaGq6gUdCq25gqmWeLFPoUKrsDKABFcIaVPPTdwY7Z03PY6JekGupsqzAHCgqMJmIjBrWvh5wNwnh+qfDBhQU8ODmfhvFqVlf3jpBdGCQwUIGRDERLmfhLXl0mFbRoDB+jo6Ia0w4P3vZ3C/jDkpeuWlA4gsU3Bt74NChqf+YygsfFU/dbfOELAzJmnexdvLnJ52LDp1KwlhPgJrvUpKYToA/4Prui5SwjxYVyH+vVTh/8aN4PwEG4k440AU0LqH4AXdv2lFwLecasV3I5rL75v6sFx1jgpeALLw8PjjEVVVZqWL8caG8Q8dBh7KqHOtkGq7o1UCAiFIU2ITDjKM8Mzifb2YaaH+K9/eSd/9YVvsUXbwP3Bt9NlzyalxXACgoFKPVgKDcFB8sUozb6j9BstKMKhNdhL8nAf+sEUoSt9TBxM45tfYkg2UAzYKFaROiNIQ7GeWWWTkVGFB3a3MzkBSkW6jZQLAupwHRQarnVoGHgOV0DFcLP8worbfqfMb9rsgOOKMwe352EESIMZgkoVREJgKGA60FuGzTlIVWBzBpYng3yovomofpQ7kknSdZNkh6KEE2UqkSjh8Tzr5v2So0Y7z+9cTPZAzC0hYUs3+twBpGuuskvws59bJJMKX/jC609UeDMjJQwMQTwKodDp3s0p4tRlEf7Bq/zoslc4VgK3vMo8twG3vcL4ZmDRK4yPv9IaJwtPYHl4eJyx1C1dSnZoiAUf+giHb7uV/iMDWDbkK1CTAKGBzw+xJDxw6XUsKW/j4dQFBAd2M39lAXn7U/zhd/8/3vGZR5hh92IFDYKimqey51IqRlkS2M6qyY3sqFmB5lhowmKutptl254h8B87KayeweG31+FviHGO2MhBOQ9/TYFDXfMYHsoT7h9hwCgwElrAUKGWgPRDWSMzrEAH7jusjnvjiuNWe18MpCQEJHQJOAjCDzKHa0Gqnjr5GG4sVgU3NqYWfNWS66cqiV/UAPvTsDcLeRvq/O7hApUm9Vr+uDbDnJsOs3XjJOsf0BgphOnPBtESDvNjh9m5Zyld/R1gKiAhERxHU8qMpuqQMeHuo8tt8vzjn0huuEHS3Pwm8hO+Abp7HR57QrJzj6CpSeFTN7sNsM9mwkFYM01h6U7DOHoAACAASURBVF4vQpez/E/Gw8PjzUz9kiX0rF+PABZ+8rMYP/k+vVufxzYtJlPgT0CxoYqxTyxl6bV9zFUaqXpfgu09C7Fyo1zy4UlSE/fy4OEraV0+iSMVMqUYs9Ue2qqOMJNDtOwcYqhqhHsra7HyCju3Lmb36FzGL6sFaRDpzjHTOEhTpJ9mpYecL0CwNc+hPTF2qws4t/AMWnA+O9BQRJHCQBTapyqyx3AFUq2Eaum6Af0Wmr9C0MhhrDYRKYX8L6JUtoaxRwUSxS2roEnXsqWBGJA0nGdz3SLBB5pdS1JjED6ywGbLmML944K4Dy6bijkSUkG33dY+b1uT4/zzymzut/nOVpWAkWFY1tL7s3ZkJxCzqY2NcmF0A4/vW4NfL1DU/RBSQXFwbIXeXsk3vgV/97npySQ8UykWJX/9RZOHHnUYGBSYFcmaC+GWm3xo2tktLnN52LDxdO/i7MITWB4eHmcsRjjMsg9/mOd/9CMyg0PUX3Y1iXNWk+7ZS6h8GP9FNsqlEZS6ClpkNTPE5bQsWsM1t91Ceqib0b1/x/X5cR7YupnRTUkisx0ua9lHoLVIrJjjCE2Yqp+ebCOHu+ei2YLt9vk0TBzkT3/wCW67/OvsPNxJ/1gjK+ObKccMDKeECEoSDWVC+7Jc/Pu1/O8Wi5CwyEmBUyXd2CobVyAlJDRNBY8FLfyRIoZuogqJWnKQEUH4PVnKloo25tCumGQmQ/RZCmVDQdEFTYbk2nbJPy3QMabu8xuYYKc/S7JZ558b69ClSuAFL17xGcjezbzIxWwLRpksCQI+H8tjMZ7P2mzOrKQ8GcB5TMeor9Apt5E+GsOR4EgFRh0YcwPcFAVUVZIvSPYdFKw6hRXfTzVf/5bFD39ikpsUVMqg6BpPPgW791isWP46OmO/WTlFLsK3Cp7A8vDwOKMJ19Vx3p//OZNdXeRHRlANg6qODowqnfLw0wyJfZSrIoRFOw2sAkBV/SSa5hHsvwnf/V/i/VUHyBafwJ+yGGisoTAZJKMESQ7m0RMl7tryh0REloZYH++6+0vM/dl6oppOMenDigUY6WmhKz6Oc7mgUfRhjlewSzpOIkKSPEubJ3i6O0k670c2TjVtNgG/RG8vM7d3Pz5KjLUnSCcjYEO5GMDRVCKxImpIcN6f7KFqe4F9u5pobx5gbk01kxE/VinKQk3wt/MMwlOpfHksdpIhlPOzO28xJ1JmWTD44kUTfhAazaKW7NgCbn+ygp0P8d65Ou9ebNHFAooXBhi8K4o2AEf1dlZWP0fVSJrUSJUb2DbVRk3XFaoSDsHw71QvdtqQUrJnT4XNm0vYNixb5qOz04eqTo9lacc++O4dFvlcGUUDUTGQZgXT9HHPr85+gRUOwZpV0zOX5yJ08QSWh4fHGY9QFBIzZ5L4rdSwYN2VdHDlq77Of85VVPWnmbzru1RkkEkzTUHLMb6umfFyDZ3GTu43ruKSVRvQtQxLvvId5m3ajiP8DDR20hOZ49aBkJAeqKLkDFAqagzma6nUhFgnNrOrdyVbxpMULQUEyLKCIgFboqgm1z91F4tH9yKDEv1QhR+f/3scapuFotk4qNQqA+hhSVpIFl46wQVXTFIy0/j881DKjXRMxhkYUvj6BnjHQji/A3yoWGN+7nzCj5BQVg3q1kDjCwnrgRXgWwiKn0AWyIAiIajA2tpmoBn7Zvg7G/7nEbDlfGKd9VyxSOHO/9bJTTo4thvjJsISo1UlXRGMDjl87WuSchk6O2HNGoV4/NS4zh58sMCjjxaoqlJRFLjzziwHD5q8733TU339UC+E/BK7pKL6QKAgpY1hOJyilr2nlVwONjxzundxduEJLA8Pj7MXIYi9+/fQzzmf4tDTCN8AkZoJwj2bCDsFvjv3QxQdH76t3cz5xg9ZKPeT8Csc+MAqvnXtl5F3SKhIFJ9FxyX7yVSiLP3Gr4gnZxDOLmVH9AIOkSQZG+fK9jH6HsjzTM0yjop6HN2hOjvBQKaF0UQr7bG9JK1x3rHlPm7t+AQ4Ar9SxMAibBeoM0bptoOE7HEi6Fy9czP1ai2FGWv58tYItWF4/KArsDQE/n01LNQtmsIqubTGs0fgumPdd4pbfKutFv5wDWQKsPKYzq+qCv/0CfjiH7vGKl131dnq88r88EcVDo4aBAyH6jq4aI3Klucc7vhvWLnU7SO4ZQvs3evw8Y8rRKMnV2SlUjYbNhRpa9N/Y7GKxRS2by9zwQUBmpre+K1sxUJYdo5K1yEbsyAQqo3uF/g0m8svfQtkUEpOd6HRsw5PYHl4eJz1BJtbmN/8e9i4dR6seouHHvlv1ty2ncJTD9KQPogzbLKzEOedWppi7xizxW46bujGyur4ZlgMBRu4ouc+5hpH2duznKOig876esLJhUhMHCdHTNlAjXmApzvqOVpSaOw5SmLyCOPhBezJL2KBfycdlS4wHRzHwNQCCCmIKxPYIkhMRDlk6XSO95OWKk3DOwmbBZY138COfrhqPvxsCA4XISZUopaKT8I+C2Yrroi6dxMMp1wxdcEC19vX2eZeBylhcAxq4i9mxR2bHSel5OA+k+Xnaqg9gkREYWJcUspLKllBsRoCAVfgNDXB0aOSLVskl1zijo2MuGtMd/PmsTEbIXiJO1AIgRAwOmq/YYE1OFhhy7MZ3nWRYNVcHz+5y2b/QVAVi/dea7D6gsAbPYUznnAY1rxteua649vTM8+bHU9geXh4vGVQp97yVDTWXvgHjFYv4ZDTQH50hHSpQOOv/4fetKT+wT1cvfOL9K57J6OrZ9Hlm8vM8hH+YMsP+OHcGxk1Z3Ne/xDKuEBhBeBHJsOEL72EaCjErACkyuN09+8ilRkmMhYi29jE0Egj+SYfMiNxNAfT76M304Ht12iP5AmoOpm0zeHuKGMBIFRH/1gW2Qy/twrm1cAXD0POhoUz3Myvh8Ygb0CtgIkNMDIB8SD87yaIBqGz/cXzP9wP3/oZ/N7lcO5rtJA51iuWTUsEAkV5qaUqFoMDBySXXALbt8Ndd7nj73kPrJjGYPhIRHlVN10k8sasZ7mczXe/OwRAuSyZMcPk3l/Ucscd4zz3XJa//IuGl5332UguBxuePt27OLvwBJaHh8dbEtUwqF+6lLolS7CKRTS/nz0LFrLj7z9BUIOm0QHO2fpj7rvsIywf2sm15V9zUG1nZ+k84h3jlIKjhGJLfjOfEAK9thYAqaWpatyAYmTILRZ0b1eIFobZH2lhf92FKKKCpoHuWJSln1SlDtXqo5Ivs+97OsHxKkRxnAVXDHFn4APkR2HXCPz1anh/PTydhSccSCyG1SkYLQMKdI/BzGpQBAQNGJx8qcCqq4KLlkF7wytfEyEEl11m8PNflimXFIYKkpqkoCqhkitKVjS99PhyGZqnur7t2eNaQYRwv59OgVVXpzFvnsHevWWamnQUBQYGLBobVdra3ljw+eSkRbksaW31IaWku7tEJCK48cYEa9dGaGvzTdNZnOF4LsJpxxNYHh4eZzSjPE+GbupYSZjp79cihECfysBb+PFbaEw8x65bf4xeJ/BfHuKmw7eTCE0iG3UeNa6g2jdKSfFTzE4QjB5EOAtBeakLSRrjoNjEapcSax4j2LSGu8Zg3BjnyvivaagZ5KC6kD3WEkKKj7AVI21V0EdGMIsBqufr+PfGOJpcSn1yNrtGoTYEugoLI/BUCapst0XO1Y1QKcKMIGwchz1HIRaEQsWNvzqWSAjeteb41+PCC3XiccFTmx22dWlUJVWkhNltkkRYkkpJTNOth5XLCVatUgBYuRL27nVdhCunqWDlsVx/fZjHHlN57rkSti1ZvtzHFVeE3lAWoW1LDh92KJXg6NEypilZtCiIqgqCQZWOjrdA7NUU4TCsOX965rrjZbXV35p4AsvDw+OMxaTAMFvR8DPEZmZx7UlfM3bhJ1m+YQO57AjhyhBaAJwKHCrNYDeLaK46Sp9sQXdMJsppklNxXccizDgSAf5xHMvHPeVJ9LYRbqr5FdX6OCmRIOnbiFIK0lOYQ84JYFGL0RiEwATBowa+eJz6BY0srFU4P+MKLM3VMiwLwi9SEFZhfhBqYu548/lQvQtG07B2Bcxtfv3nL4Sgs1Ons9NtSZTKQsAH42MK3/ymxYYNNuPjJjU1gi9/2U9Hhytw5syBz37WFViR6Unsewl+v8LatSHe/vYgUjItbrsjR2x++UsTIaJccAFEoyorVoSnYbdvPnI52PDk6d7F2YUnsDw8PM5YNHwEqaPAEAnmveRnslKGvi5I1CHiVa8yw+tHaVyJ78Ofxvr+d5l8fIhibY6xa2rZELuKaDHDpFZF82Q/woFxf5ikeHkAtDATMHopaBmeLBQQDYMsiuymOjCOic6EqCJiFSmPC1LlCBagVxSUerju49WsHYgzo95HQ4Prnur4rdM7JwTNGuQs0I+JTQr4XGF1IqRt2FKEmArL/K5b8bdRVaiOu98HWwSXXgr795fQNBNVtSmXFdJphVjMVX7h39Im+bzD0JBJU5OO36+c2MZegxeC26eDhgaF+fM16usNrrrKh5iuid+MeC7CaccTWB4eHmcsApU2rsSmhM4xHXczY5jf/nOKAztRfFGCH/4v1NkLpm1dbdktROcuxBi4h7KSJ5vfxHiqlnFfgpbRPmozo/TWthKIrHCLRb3S3s0qpBljOLqRss/AMRR6rRaataP4lBK9mUZKtobUbAKGibQ1qjIxPtcQgbjKrqNweB/Mroe6+EvnHirB97qhaLv3xWsbYFXi9Z3jD9IwZIIp3T7Pq4Kv/Zply1SamgTj4zYdHYI9e2yefLKMrmt87GMqDQ0viijLknzxi108//w4q1fX8YUvzHh9GzwFhMMKN998Aif+FiAchjWrp2euO344PfO82fEEloeHxxmNgopyrLgC5O71TJa2o4xnkOoYzubvEpv9b9O3qFAQoUsJzFqN3x5HfPvf8Ju1BBt0BpOtjASbSYZm0qS2v+oUWUdyr5JjKBHC9jkcUGfTrzRzgLk0y6NUHAMUlVrdokkxWKmZ2MEDPGwZ7N/SwNhoALUY40FUPno5tCRfnPvn/W4weUsQKg7cMwjzIhA9wXhvR8KoBQ06DFsweQItUsbGYMOTCp//fJRs1o+qChobNf7kTyocOGCiqg5/+7cvBoSbpuS55wZRFIennhrCcVrfEtl4b1ZyOdjwxOnexdnF9NhsPTw8PE4lqorZ4EMtOAgpMZtPUtyMMBBaA7Ou+zRKpp7wriBNz6jMy85jUWgxAfyv+LKjpuS/jCxbNYvipI7iOEyo1aTVKBk1TDdt1CuDBEJ55kf6WBw8ArH/oUpbz8CR/0ZXvkty7hNEVzyNHsmyfs9L55+oQHTq47HhFpAn//JQsFdFEXBtxLVg6VlYegLCrKsHHt0AR/sFpin5z/9MsWlTkauvFqxYIUgmX3p8IKDw/vc3Ewqp3Hhjsyeu3gzIaXp4AJ4Fy8PD402IWHgx4fLTZOo2Q1Ud9ed+7KSuF2lo4Nz3rqV8sExzlfabrMNXImVJbq/kmFQsZApkG4RFBrVkYToGKX8VVU6KsUAN6zL3YiszKPuGKYso6sQk4bEBjIlmtEwX/W0Jih3bSaeXUCKIf+ote2EUNk1CYwBSJkQ0SBiv75xWBmDyeXj4WfjVDPjwuuMfv7QT/vJTMKMVCgUdw4DvfCfNV7+a5LzzfNTXv1xA3XxzGzff3Hbcebu7i6RSFq2tfhKJs7vf35lMOAxrXiPD9ES5w2tGCHgCy8PD481IKE78gq8QNidRjDCKOPm1ipY0+3ii34d+nJCdgQp8v7fEeL1F2MzS1tGNiEjKewxkUSUwI4/QHQ7KdlpCA/irRglqFmOOwuRkhN7sAsbNBA3+NBUlzKBmUohnqG3ez68JchkzieHn6nrXRbg3A/U+eFcj+H6HigKTabBsGB53s/+OF+Ot6zB7qtWOYajccksVd96Zwe9XqK9/0RlSqTj88pdpLrwwTH398QXTtm1Z7rprBCEEwaDCLbc0UVXliazTQS4HGzac7l2cXXgCy8PD482JoqD5qk/Zco1RaI5CugSxV/AM5mx45KhJz36FmplZOqqPUND8KMMO9TOHyPn8HNk8m8lNdRCA3s5Z7KhZhpXzk94TwxnU0UNlEh1pamuzLKrrp6o6S5uhMNsfoojNZvq4jFn4VFjX6D5eL44DzzwPuw5BMg7XXgRzZxxfXL0S7e06n//8y6+/qgpqazUCgVeOQLEs6O6GZBJ27MhRVaUTj2vs2ZNn/foUV1yRIBR669Sf8jh78QSWh4eHxwmypg1+9PwrC6yjFejbPwH5OI2JPqTfoX5yCKXOoqgF6XuqlfL+ME5Aoi81sVSNscl6dLWMMbdItiqAmTMoHIlQnpxA1yQLE0dY6A9joCNQyFJ+2bq2A/uybibg3MhrW7K27oN71kNNFXQPwNs6oT55/Ncci2lK9u0rM3u28YqlF1RVcNFFr14I66GH4PHH3TY7y5b52Lu3wOSkxebNGSzLpqurwC23tGAYXojwqcRzEU4/nsDy8PDwOEHmJWFeNRxJudasY8nYAEWClkFAL1IbHiR1OIFeUQkmC8ys6sFa1YcldAbUBhYE93BuejMjIsnd1rtQGyW6UiGUzbOqahORcJ6qUpm62HlIJClKzOTlFqN7BuDZMTdwfWYYbup45ZpWL9A3DNGQ+9BVONL3+q5BT4/J7ben+dCHYnR2vnKQ//HI510rWqkE55wTZ3zcZsuWNPPn++nsDNPbW2Jy0qSu7i3SouYMwXURnkA6qccJ4wksDw8PjxNEU+F9i+D2bXA07YqsF1xrMRXqWyYohSVMSvRqi1xfiFnnHEFqFh0zjhANp8j5wrQN13LOvm1sdxbxkHkV8+bt4fLEwwS1AqOyjj39sxlIN1HnG6fgbKKnciH+cg0hrZ5yAHzHGHe2TUJbGFQBXXk3mzBynDCmjibXRaiqkMrARa+zrU17u87HPhanre11RtVPsXYt1NdDU5PkjjvSfO97aXK5Mi0tNrW1JRobfV6w+2nBqzQ63XgCy8PDw+N1EDTgpuXwP7th7yj4dagJQosB+5urUVp2YSg2JTtAvHGSRt9ROqt3kpRjoMAOOomWihT0II/mLuOcWc/wrvi9NDOIYyosVXbR0DTA/UeuJV1q5n8ONNKfnUMRh//SiswOOnyqyWBWuEKMIHMiGrvSDuVAnmREIaiGyJahbEEy9PL9L54N77Ngbze8bTGsXvr6zl9VBXPmnJh1SUrI5iB6jMcwFILVqyGXk/zwh2lyOQtdN+jtNfnyl5MsXx5F1z334KnGdRFOTykNz0Xo4gksDw8Pj9eJX4c/WgJ9Gdh4FHYMu8U7W61aJrU4/uAI+ckA8c4UC3x7qDOHUU0LU1EZ8dWw3N7FrmInsVCWtuBRZsg+hqlFoqA5FRYo+3i86goeSy0ib0aoMvIYKuBo7Cw6fGpgmLUzRljg07imZRHlWIpDxgQRPzxRaOanz4QIOPDHK2Hmb3kVhYAVC9zHC1gWPPms+/XC88A3Td65+x6F9c/CNZfBhW/7rWvoF2ia23AZJKGQSmtrEJ/vjYurQsFh61aT+nqFWbM8a9iJ4LoIX0cxNY/XxBNYHh4eHr8DQkBLzH1cNRt601C2Ahwoz+JnwT58AQtNqxAyioiixJQaORnGxiDiyxGozRG1JrFNCITySAdQJYZmkidIAR/jhWrA4EBFA0UQCpRAQM7U2JuOkqkdRlf7aKvyIRGM2Db/Wshx0CeoHA0g9wr+eiUkXiNUas9++NUD7jkFg3D+qum5RuOTUCnDRNp9vmmTxb/8SxF/QPDFvw/wjW/U8+lPD1AuW/zpnyZpbJweMfT442Uee6yMzyf4q7+K/KZXosfx8FyE040nsDw8PDzeIBEfLKx1v19OHcvSTXyFXaQcP2UMpKGgqw66sKkIjbKqU6ONcHHocXaXFzBs1TDLOExeBPGbJTZkzyczkcR0AtiKiq04GLEsaqyAYgtKWR+TJY0l6AxRoYYg7YQ54lgU/TqB9jKarqAKP3tSsLr++PsPBtz4MgmEprE137vXwopO6Gh1n3/pSwUyBega1Rn9izIrF6vM6KhnzkzJ5Zf7p63ZcjSqoKoQCgkMw6sgfyKEw4I1a6anPIbnInTxBJaHh4fHNDOXJj7f/xl+0bqEMaeaiJohpBQJ2CX8ssTT+U6u3HU38XAz/oUlHitfTKf5PM3OAIcqbQwSQZsYomAIUByk4hCIFSiXDELBEmFDIaIHmU8TEjhMjg/QwYRaoEc3KQcl1SHwlaH1BLoIzeqAP77Jze5rn8aezOEQLJjz4nNVBccGy5Y8ukflvi0WjAgoC3784xxPPBnhsV0K+4/CxUvdx+/CBRcYtLaqxOMKgYAnsE6EXM5hw4bK6d7GWYUnsDw8PDymG+GndmgOy6Lb6Im00JVpI1eMYgZUFuV38ORjrWyIrWBFqJckE1Q5GSrSx/rQhTxTey5NZg+xsXGkbePYGppQsSsauq+CJhV8CDpiEm2qnWxeOmyXFS4RfuIBgT+gMCvkI6hA7AST/dpaT+L1mOLv/z7I179eZNynMNhjw6AKmgQVDh0y+M5tJoWkj5YaeGgzLJ/tlpN4vQghaG31bm+vH89FOJ14f4EeHh4e0024FjV0DomH9iPau9m6oBYlaHFOz3aKowZL//N79Lz/Yn5x3tUUa5uIyRQTehWmqiMcQUZLUF07zAw5QNdgK5WKijVYRSBUpNonaampsDhoA4IyNuOOzu1OkQ+rIa4QUz4+Pww4Dr22Q1wotCinPw5p6VKNz/1jhGs/Y8PzUwHVmgAkmJJ8BtQaGJqEUAB8Xnz6KcN1EU7PBfdchC6ewPLw8PCYbhSV0PIPk7f7OdK1mYQxSqySo0dpZnxzBvGNqyhdtJyIUOmxa8gQQRESHyWKjkpQFhmLN1Kmwszk81iperJFlaBRImHA1XGVKhQcJOOUuUqpQRKgDZWMBUEVdtsWP7MqKLhV3q/Wdc7TTo5iGRws8vDDw7znPc0Eg8e/rfh0aKxTOeQTIABLQgl0XXDLxxVKAo6OwqI28P1upbY8fgdcF+HLOwV4/O54AsvDw8PjJKBofkKxpUT2f5/990XZV9dMaOx5OqweJt53NWPCwRKCFtFLj2xDlRaWVPHZFeJO2h3TJRknQt6G8vogHSvGCEYy9FkquxUDIRRWiChLRZRhS+Vbo1BwICgkkzGLdp+CTwhMKXnQslihahjTFEh+LFKCbTtI+drHpodhZQKc83xse7pEaVghXiv56r/otLToCAF1CfB74uo04LkIpxNPYHl4eHicJPpCIaLvSDL7qQLDh/t5uO0abr38JlZFd7HY2UVBDVKjjlPFJEJKFNMia8VQsemrNJMy4xhqBS0syTVq7N5Xx9JLh3lCBtHKJjHHIKQrPKJNsG2kBp8CSQNSNmwZV2ird0AFFXA4ebfPxsYAN9zQ8ZrHDQ3D7T+Gjdssdu2GmqjC7Y9pPLpL50AaNu6GooSHtkuECR+7GlrrvSD1U4HrIpyeAmiei9DFE1geHh4eJwGrXObgr3+K3pwk+A4fP4/ewtFKC82VFN2pGZyrPkcwnKckAqjCwrEEQ7kmouU8vbSiBh18TgUbBWmrqK0Ozb4eamv6iIk080UPABP2PJ6tXIkpq6lR3TiruCqoR+GgZdEgBDkpWaZpBF7FerV+fYWDBy2uv95/UmpGSSmREvJ5waEe2PicDVmT/IDD1W83ueGzCsGwyuA4bOmVPPQ9i50bJf/5GcE/fVnj3deJ19WQejoolRx27KhQqcDChTqJxPSUMDhTcV2EhdO9jbMKT2B5eHh4nASGd+xA3bIbxxYcHkngu6ZEp383JTvAI0cuYafdyYznu1GSNjktAiiElSKHx2bxWPFS4gsm0BUT6ejYFRU5qrB08Q6iSpo2ulGEIONUUaMeIG3PJBPKM6Do1FZmYNk+ZioqVxo6Y4pDg1BYqb762/3GjSa9vTYXXuhMq8AqFGHDMxWefaJAuSLR2oMcMnTodGDYDwUojNn84N/yXHZdhAa/ID9ks2UDIFUqJfjzP7Pozut88v3QXDdtWzsuliW5/fYcPT0WqirYsKHELbdEiMfPZpHlFRqdbjyB5eHh4XES0INBAvtNzE4VvW8US/EzMF7DkeJM5GE/+w4v4Jcj72bhjN3oMRtLU+nXZjCeqgEpGYtoJALjJMUEVtYgEUihiwplfIxRTZY4KBoqDhEjS1O4iueLeTKqJFiay/VJwZLjpOE5DpQqEPDBBz/oZ2TEYdas6RUQ//uo5F+/5bB8roY/7PDc/QUG4mHoMSAgIAkoCrkRicxbjI7phBw39l3TJY4DlikwLbBOwb3/4YeHWL8+zcyZVRw+rDB7tusy6+426eqyWLbs7BVY4bDCmjWBaZnLcxG6eALLw8PD4yRQs3AhCz98Mxvv/wr1KytoFOk3F2LndfSBIuN7E2jnmxzMzKE0GiJcnSEXrQIfYAnidoq1wfv+//buPDqu8rzj+Pe5MxqNRiNrl21ZtlXjrcYb3oqDK0xYAqVQF+pSSktpacN62oayhJMu55T8QQ40TQmUhLQ5p0lzcCGtT2loSUMCKCwxi3djsxjb2MabZCRLstaZt3/c6yIbScjSlUYz+n3Ouceeu81zH0u+z3nfe9+XIq+ZyqnHaE5NoJ08GtxEmrwKquwoaSIcYSFLrZKp+THKoh7d6S6+UAIlA/zv3tQK//I8HGuGmVPgdy+MMGnS4IqHjo40e/d2MHVqPoWFAx8zpcrhUo5kgT8FT1va6OgCEgbn4ldSbUBDhBcOpZk4G9Zc5JF4LEVro2HAxZcad14PtdWDy/tQvfVWC/fdtx/Pc6xaFWP3bqitjfmDo6b9uRNzmd9F2JrpMHKKCiwRkRFgZhTcdBHpia+S/uAD5u7ZwM78JVy5/9+5v+lB1aVXQQAADnxJREFUNseX8/Wf3M2WK5ZCOkJ+dzt5J7v4OL8KPKMu/+ekuiKkyoz2SJxiayYSSdIRiTHJixOhhnKmEqOcJCmaOE4k4jHxUC3feQs6u+FXZsHn5/sjqPf26tvQ2ALTqmDnh7DrACz8pcFd1/r1jbz1ViuzZsW55ZbJA+67YI7H9OooW3akWTynh8pphZzjjHeKHHgGux0cdNADLds8tk9McdvNEa77R9j/GsydAd+4w2MEXnw8zYkTPaxb10BJSZxIBMzSTJ8e4fDhHtJpWL48nzlzcn1QLnURhk0FlojISEmnKTm/gsjxRq7cUE/zrGru/fZdtLQX8d/ROrYkl/j3tGJoopSSxibK8w5xIlrKhNqPaW6cQG3l+5S7BsygM7+Qcq+UOcwgSQKPKEfpZBplLKSWPYeM770UZWIxFMTgp9vA8+DiBXCyy1+KT/UCDWJIhb6Y+YvnfVL1tLamKSy0T80lWJSEO26O4lLGjGn5vH0swsFjxmMHU2zZkoYDQEcQSKOx4Vn4i3Sa8l/2SFbAnb/NiBdXp64pP99j2rR8du1qp6kpxe23T2TevAQ9Pf7chrkumYxQVzeIeZUGQV2EPhVYIiIj5BxvKh9FKnn9UmN93mKueey7uE5oTk6hvvtSmGVYdxeuLEaqJZ+2mgTR1hQF7S0cOlzBwuptVHIMz6VJJtrwosVMZyL7iGFAN10YxkwSxMnjvQOQyPcXgMmlsHkPTKmEdRshlYaSArh2Aew+DPuOwvzpMLfm9Lidc/1OvLxmTTlLliSpqfEHqtq3r4dvfauNtWvjLFly+mv+ngcXXWCcutXU1sKPfw6Xr/A4/GYPR7oIKjbAOdIn4dWX4U+XQiQK3SeAqrD+NfpXVBRl7dpyXnyxmbq6Yq64ooRJk8IZsiBbtLamqK9vznQYOUUFlojICCmliAsLb2ba0QdY+fYjdO9twaUhku7mWLISKjymLd9P04fFNFeW0ZEqhMIUXiLFq8dWM2/6LizqiCYcbdFiaq2Qc5lFB+18TBe1xFlGCWX4xU48Bt09n3x/Vw/E8uCpTX5hlYjB4RNQvxfu+HV/e+8BPZ1zPPNMN2+80cOqVVEuv/zTo33G4x6zZ3/yMHRxscecOVGqqgb3DFdxEbS0gHNGXh50dwMeGIbrAZdyHG+C+XNh2sA9kKFatCjJokXhtOBkJ3URhk0FlojICCqOTGTxjIeYO2ET/7TZo/zAjVSe2ENiYg8nElBY0U7pzEbe3ZFH+zsJXHOMdFMeJ5OwZcUCqgqP41FGrfVQwEr+gzZiQCcREhRQwSdF0PJz4M3dsK8Boh70pGHtSnh6u19cAUyIw/E2v3XpzNHSGxsdGzb0UFNj1Nf3sGpVHsnkwH10JSUeN900+BmZVywEl4adr8PGVmju9te7lAMzKid6fO0eKC32Y5TRogIrbCqwRERGmldAvOpz5E9OUX/hTnZv+l9irhk6q2hoqGBqzR7KljcRX/ERXW1x0ilHobXRVVnAhyeXUBltwosmabT5JDFKiZLCsZGT1FH0/19TmoTbvwDb9kFXCuZUQ3UpbDgI+z/2n79qaIPL5vYd5oQJRkWFsX+/Y8oUoyCct/ZPT4UHK5cYf32/x133OHbudHR0OMAoqjK+83WP8tLwv1cG5j+DVRzKufQMlk8FlojIKFmxwvjmdx2ve4up3rOFhtkzaXy5isnT9pFX1kV7qhAv4SjNO0bUdRPvyeNQzzIOdXawOFpEVbyIjzhJKdBGmiI+3S1XUgi/Ou/0db+3DJ5/B462worpcEE/s9rEYsatt8Y5fDhNdbVHJDJyT5ivXu2x7geO9c+k2f62sWiRx9prjOlTR+wrZQD+M1jHMx1GTlGBJSIyShYuNFZfCs/9axnH85aTd7SB7hOVbPv+CmZdtYPJs/eT57pJdUTp7imkwKsind9OWVeEKR3VLIjHOEg3B+kmgcfVDK7FoTAffmPh4GJMJIwZM0ZnQM3Zs4377s7dwTuzi8OfsVLCogJLRGSUmBk3XGnsq3L8zYsJUpEYHIL0zijv7F5E0aJGJl/4EbFkFxMSHpEJHUzvKKXgWDUVM/JJAL9DKe2kyccjSm4PfimjJ5mMUlcXTt+sugh9KrBEREaRGfzZ4jhbjhzlZ+8aLcX5pKIRUo1xWraV07K/jOKSTm5Z66jpyqOjM0bag+nBc+QeRmEfXYMiw9Ha2kN9fUOmw8gpKrBEREZZMgZfWlpFPp1s2OnY15BHKuZBwlHS7XHDBQmK03C4BeJR+MNz/LcCRUaO3iIMmwosEZEMWFkNlYl86mfAh00Qd1A31ZhZBVuboLkLpiRgfgkkc32WFsk4v4uwPJRzqYvQpwJLRCRDZpb4y5kuGYHhEUQG4ncRHs10GDlFBZaIiMi4py7CsKnAEhERGef8LsLKUM6lLkKfCiwREZFxrrW1m/r6w5kOI6eowBIRERHURRguFVgiIiLjXDKZR13dpFDOpS5CnwosERGRca61tYv6+oOZDiOnqMASERER1EUYLhVYIiIi45zfRVgdyrnURehTgSUiIjLO+V2E+zMdRk5RgSUiIjLuaaDRsKnAEhERGeeSyRh1dTWhnEtdhD4VWCIiIuOc30X4YabDyCkqsERERMY5vwVraijnUguWz5xzmY7hM5nZMWBfpuMAKoCGTAeR5ZTD4VMOh085HD7lcHg+K3/TnXPhTA44CGb2HH5MYWhwzl0e0rmyVlYUWGOFmb3pnFuW6TiymXI4fMrh8CmHw6ccDo/yl/u8TAcgIiIikmtUYImIiIiETAXW2Xki0wHkAOVw+JTD4VMOh085HB7lL8fpGSwRERGRkKkFS0RERCRkKrBEREREQqYCqx9mttbMdphZ2syW9Vp/qZm9ZWbbgj8/38exz5jZ9tGNeOw52xyaWcLMnjWzXcFxD2Yu+swbys+gmS0N1r9vZo+YmWUm+rFhgByWm9kLZtZqZo+eccz1QQ63mtlzZhbW2EBZaYg5jJnZE2b2bvD7fO3oRz52DCWHvfbR/SRLqcDq33bgGqD+jPUNwFXOuQXAHwDf773RzK4BWkclwrFvKDl82Dk3FzgPuMDMrhiVSMemoeTvceCLwKxgGe+D/fWXww7gr4C7e680syjwD8BFzrmFwFbgzlGIcyw7qxwGvgIcdc7NBuYBL41ohGPfUHKo+0mW01Q5/XDO7QQ4swHAObep18cdQNzM8p1znWaWBO7Cv8E9NVqxjlVDyOFJ4IVgny4z2wiEM/toFjrb/AFlwATn3GvBcd8D1gD/MyoBj0ED5LANeNnMZp5xiAVLoZk1AhOA90ch1DFrCDkE+CNgbrBfmnE+4vtQcqj7SfZTC9bwXAtscs51Bp8fAP4OOJm5kLLOmTkEwMxKgKuAn2YkquzRO39TgAO9th0I1skgOee6gduAbcBH+K0v/5zRoLJM8LsL8ICZbTSzp81sYkaDyk66n2S5cd2CZWbPA5P62PQV59x/fsax5wJfAy4LPi8GZjrnvmRmtSGHOmaFmcNe66PAk8AjzrkPwop1LAo5f309b5Xz47AMJ4d9nCsPv8A6D/gA+CZwP/DV4cY5loWZQ/z7Sg3winPuLjO7C3gY+P1hhjmmhfxzOC7vJ7lmXBdYzrlLhnKcmdUA64EbnXO7g9UrgaVmthc/r1Vm9qJzbnUYsY5VIefwlCeA95xz3xhufGNdyPk7wOldqjX4rTA5bag57Mfi4Jy7AczsKeDLIZ5/TAo5h434rS7rg89PAzeHeP4xKeQcjsv7Sa5RF+FZCpq/nwXud869cmq9c+5x51y1c64WWAW8q1+GvvWXw2DbV4Fi4M8zEVs2GOBn8BDQYmbnB28P3gicbevDeHcQmGdmlcHnS4GdGYwn6zh/9Or/AlYHqy4G3s5YQFlI95Mc4ZzT0scC/CZ+i0AncAT4cbD+L4E2YHOvpeqMY2uB7Zm+hkwvZ5tD/BYXh39DO7X+jzN9HdmSv2DbMvw3lnYDjxLM1jBel/5yGGzbCxzHf0vrADAvWH9r8DO4Fb9QKM/0dWRhDqfjvzG3Ff85ymmZvo5sy2Gv7bqfZOmiqXJEREREQqYuQhEREZGQqcASERERCZkKLBEREZGQqcASERERCZkKLBEREZGQqcASyXJmFvpksGZ2tZl9Ofj7GjObN4RzvGhmy8KOTUQkG6jAEpFPcc4945x7MPi4Bn9OPhERGSQVWCI5wnwPmdl2M9tmZtcF61cHrUk/NLNdZvaDYKR3zOzXgnUvm9kjZvajYP1NZvaomX0OuBp4yMw2m9k5vVumzKwimM4DMysws3VmttXM/g0o6BXbZWb2Wq/Jf5Ojmx0RkdE1ruciFMkx1+DPpbcIqADeMLP6YNt5wLn4cxO+AlxgZm8C3wbqnHN7zOzJM0/onHvVzJ4BfuSc+yFAUJv15TbgpHNuoZktBDYG+1fgjz5/iXOuzczuA+4C/jaMixYRGYtUYInkjlXAk865FHDEzF4ClgMngNedcwcAzGwz/vQbrcAHzrk9wfFPAl8cxvfXAY8AOOe2mtnWYP35+F2MrwTFWQx4bRjfIyIy5qnAEskd/TYt4c+BdkoK/3d/oP0H0sMnjxfEz9jW19xbBvzEOXf9EL9PRCTr6BkskdxRD1xnZhEzq8RvUXp9gP13ATPMrDb4fF0/+7UARb0+7wWWBn//rTO+/wYAM5sPLAzW/wK/S3JmsC1hZrMHcT0iIllLBZZI7lgPbAW2AD8D7nXOHe5vZ+dcO3A78JyZvQwcAZr72HUdcI+ZbTKzc4CHgdvM7FX8Z71OeRxIBl2D9xIUd865Y8BNwJPBtl8Ac4dzoSIiY50511eLvoiMB2aWdM61Bm8VPga855z7+0zHJSKS7dSCJTK+/Unw0PsOoBj/rUIRERkmtWCJiIiIhEwtWCIiIiIhU4ElIiIiEjIVWCIiIiIhU4ElIiIiEjIVWCIiIiIh+z957/vaz3MODwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 我们的大脑非常善于从图片中发现模式，但是需要玩转可视化参数才能让这些模式凸显出来\n",
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "    s=housing[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
    "    c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
    "    sharex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.image as mpimg\n",
    "california_img = mpimg.imread('./california.png')\n",
    "\n",
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "    s=housing[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
    "    c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=False,\n",
    "    sharex=False)\n",
    "\n",
    "plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5,\n",
    "           cmap=plt.get_cmap(\"jet\"))\n",
    "plt.ylabel(\"Latitude\", fontsize=14)\n",
    "plt.xlabel(\"Longitude\", fontsize=14)\n",
    "\n",
    "\n",
    "prices = housing[\"median_house_value\"]\n",
    "tick_values = np.linspace(prices.min(), prices.max(), 11)\n",
    "cbar = plt.colorbar()\n",
    "cbar.ax.set_yticklabels([\"$%dk\"%(round(v/1000)) for v in tick_values], fontsize=14)\n",
    "cbar.set_label('Median House Value', fontsize=16)\n",
    "\n",
    "plt.legend(fontsize=16)\n",
    "# save_fig(\"california_housing_prices_plot\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从图片中告诉我们，房屋价格与地理位置靠海，和人口密度息息相关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 寻找特征相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.924664</td>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.044568</td>\n",
       "      <td>0.069608</td>\n",
       "      <td>0.099773</td>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.045967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>-0.924664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.144160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.105623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>0.044568</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>0.134153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>0.069608</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.049686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>0.099773</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>-0.024650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>0.065843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.688075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <td>-0.045967</td>\n",
       "      <td>-0.144160</td>\n",
       "      <td>0.105623</td>\n",
       "      <td>0.134153</td>\n",
       "      <td>0.049686</td>\n",
       "      <td>-0.024650</td>\n",
       "      <td>0.065843</td>\n",
       "      <td>0.688075</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    longitude  latitude  housing_median_age  total_rooms  \\\n",
       "longitude            1.000000 -0.924664           -0.108197     0.044568   \n",
       "latitude            -0.924664  1.000000            0.011173    -0.036100   \n",
       "housing_median_age  -0.108197  0.011173            1.000000    -0.361262   \n",
       "total_rooms          0.044568 -0.036100           -0.361262     1.000000   \n",
       "total_bedrooms       0.069608 -0.066983           -0.320451     0.930380   \n",
       "population           0.099773 -0.108785           -0.296244     0.857126   \n",
       "households           0.055310 -0.071035           -0.302916     0.918484   \n",
       "median_income       -0.015176 -0.079809           -0.119034     0.198050   \n",
       "median_house_value  -0.045967 -0.144160            0.105623     0.134153   \n",
       "\n",
       "                    total_bedrooms  population  households  median_income  \\\n",
       "longitude                 0.069608    0.099773    0.055310      -0.015176   \n",
       "latitude                 -0.066983   -0.108785   -0.071035      -0.079809   \n",
       "housing_median_age       -0.320451   -0.296244   -0.302916      -0.119034   \n",
       "total_rooms               0.930380    0.857126    0.918484       0.198050   \n",
       "total_bedrooms            1.000000    0.877747    0.979728      -0.007723   \n",
       "population                0.877747    1.000000    0.907222       0.004834   \n",
       "households                0.979728    0.907222    1.000000       0.013033   \n",
       "median_income            -0.007723    0.004834    0.013033       1.000000   \n",
       "median_house_value        0.049686   -0.024650    0.065843       0.688075   \n",
       "\n",
       "                    median_house_value  \n",
       "longitude                    -0.045967  \n",
       "latitude                     -0.144160  \n",
       "housing_median_age            0.105623  \n",
       "total_rooms                   0.134153  \n",
       "total_bedrooms                0.049686  \n",
       "population                   -0.024650  \n",
       "households                    0.065843  \n",
       "median_income                 0.688075  \n",
       "median_house_value            1.000000  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# corr() 计算出每对特征之间的相关系数， 称为皮尔逊相关系数\n",
    "corr_matrix =  housing.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.688075\n",
       "total_rooms           0.134153\n",
       "housing_median_age    0.105623\n",
       "households            0.065843\n",
       "total_bedrooms        0.049686\n",
       "population           -0.024650\n",
       "longitude            -0.045967\n",
       "latitude             -0.144160\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix['median_house_value'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * 相关系数范围 从 -1 变化到 1，越接近1 ，表示越强的正相关，，比如，当收入中位数上升时，房价中位数也趋于上升，\n",
    "   * 当系数接近-1 ，则表示有强烈的负相关，纬度和房价中位数之间出现轻微的负相关，越往北走，房价倾向于下降，\n",
    "   * 系数靠近0 ，说明二者之间没有线性相关性，\n",
    "   * 相关系数仅检测 线性相关性 ，如果x上升，y上升或者下降，可能会彻底遗漏非线性相关性，例如 如果x接近于0，y上升"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a18742c50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a18664048>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a186ca6d8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a186f3d68>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a18725438>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a18725470>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a19953198>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1997c828>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a199a5eb8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1867f588>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a186a9c18>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a1879b2e8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a19cf4978>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a19d29048>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a19d516d8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a19d7ad68>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用padans scatter_matrix 函数，可以绘制特征之间的相关性\n",
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "attributes = ['median_house_value', 'median_income', 'total_rooms', 'housing_median_age']\n",
    "scatter_matrix(housing[attributes], figsize = (12, 8) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 16, 0, 550000]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind = 'scatter', x='median_income', y = 'median_house_value', alpha = 0.1)\n",
    "plt.axis([0, 16, 0, 550000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 50万美元是一条清晰的线，被设置上限了， 45w，35w，28w，也有虚线， 可能是异常值，我可以在后期把数据删除掉"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 在给机器学习算法输入数据之前，我们识别出了一些异常数据，需要提前清理掉，也发现了不同属性之间的某些联系，跟目标属性相关的联系\n",
    "   2. 某些属性分布显示出了明显的“重尾”分布，对进行转换处理，计算其对数\n",
    "   3. 在给机器学习算法输入数据之前，最后一件事儿，尝试各种属性的组合\n",
    "   4. 每个项目历程都不一样，大致思路都相识"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 试验不同特征的组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \\\n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY   \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY   \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY   \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY   \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY   \n",
       "\n",
       "  income_cat  \n",
       "0          5  \n",
       "1          5  \n",
       "2          5  \n",
       "3          4  \n",
       "4          3  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['rooms_per_household'] = housing['total_rooms']/housing['households']\n",
    "housing['bedrooms_per_room'] = housing['total_bedrooms']/housing['total_rooms']\n",
    "housing['population_per_household'] = housing['population']/housing['households']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value          1.000000\n",
       "median_income               0.688075\n",
       "rooms_per_household         0.151948\n",
       "total_rooms                 0.134153\n",
       "housing_median_age          0.105623\n",
       "households                  0.065843\n",
       "total_bedrooms              0.049686\n",
       "population_per_household   -0.023737\n",
       "population                 -0.024650\n",
       "longitude                  -0.045967\n",
       "latitude                   -0.144160\n",
       "bedrooms_per_room          -0.255880\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 关联矩阵\n",
    "corr_matrix = housing.corr()\n",
    "corr_matrix['median_house_value'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 机器学习算法的数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>286600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>340600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>196900.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>46300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>254500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "17606       710.0       339.0         2.7042            286600.0   \n",
       "18632       306.0       113.0         6.4214            340600.0   \n",
       "14650       936.0       462.0         2.8621            196900.0   \n",
       "3230       1460.0       353.0         1.8839             46300.0   \n",
       "3555       4459.0      1463.0         3.0347            254500.0   \n",
       "\n",
       "      ocean_proximity income_cat  \n",
       "17606       <1H OCEAN          2  \n",
       "18632       <1H OCEAN          5  \n",
       "14650      NEAR OCEAN          2  \n",
       "3230           INLAND          2  \n",
       "3555        <1H OCEAN          3  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 得到一个分层抽样代表全局数据集的  训练集和测试集\n",
    "strat_train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将数据集的数据和标签分开 \n",
    "housing_labels = strat_train_set['median_house_value'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop('median_house_value', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_cat  \n",
       "17606       710.0       339.0         2.7042       <1H OCEAN          2  \n",
       "18632       306.0       113.0         6.4214       <1H OCEAN          5  \n",
       "14650       936.0       462.0         2.8621      NEAR OCEAN          2  \n",
       "3230       1460.0       353.0         1.8839          INLAND          2  \n",
       "3555       4459.0      1463.0         3.0347       <1H OCEAN          3  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 10 columns):\n",
      "longitude             16512 non-null float64\n",
      "latitude              16512 non-null float64\n",
      "housing_median_age    16512 non-null float64\n",
      "total_rooms           16512 non-null float64\n",
      "total_bedrooms        16354 non-null float64\n",
      "population            16512 non-null float64\n",
      "households            16512 non-null float64\n",
      "median_income         16512 non-null float64\n",
      "ocean_proximity       16512 non-null object\n",
      "income_cat            16512 non-null category\n",
      "dtypes: category(1), float64(8), object(1)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据清洗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * 大多数机器学算法无法在缺失的特征上工作，创建一些函数辅助，total_bedrooms有缺失，\n",
    "   1. 放弃这些区域\n",
    "   2. 放弃这个属性\n",
    "   3. 将缺失值 设置为 某个值，（0， 平均数或者中位数都可以）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4629</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1.6675</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19252</th>\n",
       "      <td>-122.79</td>\n",
       "      <td>38.48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3468.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.1662</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "4629     -118.30     34.07                18.0       3759.0             NaN   \n",
       "6068     -117.86     34.01                16.0       4632.0             NaN   \n",
       "17923    -121.97     37.35                30.0       1955.0             NaN   \n",
       "13656    -117.30     34.05                 6.0       2155.0             NaN   \n",
       "19252    -122.79     38.48                 7.0       6837.0             NaN   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_cat  \n",
       "4629       3296.0      1462.0         2.2708       <1H OCEAN          2  \n",
       "6068       3038.0       727.0         5.1762       <1H OCEAN          4  \n",
       "17923       999.0       386.0         4.6328       <1H OCEAN          4  \n",
       "13656      1039.0       391.0         1.6675          INLAND          2  \n",
       "19252      3468.0      1405.0         3.1662       <1H OCEAN          3  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = housing[housing.isnull().any(axis = 1)]\n",
    "rows.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Imputer(axis=0, copy=True, missing_values='NaN', strategy='median', verbose=0)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 放弃这些区域\n",
    "# rows.dropna(subset = ['total_bedrooms'])\n",
    "# # 放弃这个属性\n",
    "# rows.drop('total_bedrooms', axis = 1)\n",
    "#  将缺失值 设置为 某个值，（0， 平均数或者中位数都可以）\n",
    "\n",
    "# 创建一个imputer实例，指定你要用属性中的中位数值替换该属性的缺失值 \n",
    "from sklearn.preprocessing import Imputer as SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer(strategy = 'median')\n",
    "\n",
    "# 使用fit() 方法将 imputer实例适配到训练集\n",
    "housing_num = housing.drop('ocean_proximity', axis =1)\n",
    "\n",
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409,    3.    ])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409,    3.    ])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-121.89  ,   37.29  ,   38.    , ...,  339.    ,    2.7042,\n",
       "           2.    ],\n",
       "       [-121.93  ,   37.05  ,   14.    , ...,  113.    ,    6.4214,\n",
       "           5.    ],\n",
       "       [-117.2   ,   32.77  ,   31.    , ...,  462.    ,    2.8621,\n",
       "           2.    ],\n",
       "       ...,\n",
       "       [-116.4   ,   34.09  ,    9.    , ...,  765.    ,    3.2723,\n",
       "           3.    ],\n",
       "       [-118.01  ,   33.82  ,   31.    , ...,  356.    ,    4.0625,\n",
       "           3.    ],\n",
       "       [-122.45  ,   37.77  ,   52.    , ...,  639.    ,    3.575 ,\n",
       "           3.    ]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = imputer.transform(housing_num)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income  income_cat  \n",
       "17606       710.0       339.0         2.7042         2.0  \n",
       "18632       306.0       113.0         6.4214         5.0  \n",
       "14650       936.0       462.0         2.8621         2.0  \n",
       "3230       1460.0       353.0         1.8839         2.0  \n",
       "3555       4459.0      1463.0         3.0347         3.0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr = pd.DataFrame(X, columns = housing_num.columns, index = housing_num.index)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 9 columns):\n",
      "longitude             16512 non-null float64\n",
      "latitude              16512 non-null float64\n",
      "housing_median_age    16512 non-null float64\n",
      "total_rooms           16512 non-null float64\n",
      "total_bedrooms        16512 non-null float64\n",
      "population            16512 non-null float64\n",
      "households            16512 non-null float64\n",
      "median_income         16512 non-null float64\n",
      "income_cat            16512 non-null float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 1.3 MB\n"
     ]
    }
   ],
   "source": [
    "housing_tr.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scikit-Learn\n",
    "* 一致性\n",
    "   1. 估算器\n",
    "       比如：各种机器学习算法\n",
    "       fit()执行估算器\n",
    "\n",
    "   2. 转换器  \n",
    "       比如：LabelBinarizer\n",
    "       transform()  执行转换数据集\n",
    "       fit_transform()  先估算，再转换\n",
    "   3. 预测器\n",
    "       predict()  对给定的新数据集进行预测\n",
    "       score()    评估测试集的预测质量\n",
    "* 检查\n",
    "    1. imputer.strategy_学习参数通过公共实例变量访问"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 处理文本和分类属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'housing' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-fae9ee45f756>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mhousing_cat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhousing\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ocean_proximity'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mhousing_cat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'housing' is not defined"
     ]
    }
   ],
   "source": [
    "housing_cat = housing[['ocean_proximity']]\n",
    "housing_cat.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     7276\n",
       "INLAND        5263\n",
       "NEAR OCEAN    2124\n",
       "NEAR BAY      1847\n",
       "ISLAND           2\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['ocean_proximity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 4, ..., 1, 0, 3])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将文本转化成对应的数字分类 ， 使用转换器\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "housing_cat = housing['ocean_proximity']\n",
    "housing_cat_encoder = encoder.fit_transform(housing_cat)\n",
    "housing_cat_encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 机器学习算法会以为两个相近的数字比远的数字更相似，比如0，4 比  0，1相似度更高，\n",
    "   2. 创建独热编码，当 <1H ,第0个属性为1，其余都为0， 列别是InLand时候，另一个属性为1，其余为0，1为热，0为冷，独热编码\n",
    "   3. OneHotEncoder编码器, fit_trans需要二维数组， 转换housing_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "encoder = OneHotEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 4, ..., 1, 0, 3])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [0],\n",
       "       [4],\n",
       "       ...,\n",
       "       [1],\n",
       "       [0],\n",
       "       [3]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoder.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_hot = encoder.fit_transform(housing_cat_encoder.reshape(-1, 1))\n",
    "housing_cat_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从稀疏矩阵 转换成 numpy\n",
    "housing_cat_hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1],\n",
       "       ...,\n",
       "       [0, 1, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 1, 0]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 一次完成两个转换 文本--整数类型--独热类型\n",
    "housing_cat\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer()\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 输出稀疏矩阵\n",
    "\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer(sparse_output = True)\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 自定义转换器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix = [list(housing.columns).index(col) for col in (\"total_rooms\", \"total_bedrooms\", \"population\", \"households\")]\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix \n",
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, add_bedrooms_per_room = True):\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    def fit(self, X, y = None):\n",
    "        return self\n",
    "    def transform(self, X, y = None):\n",
    "        rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, household_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
    "            return np.c_[X, rooms_per_household,population_per_household, bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household,population_per_household]\n",
    "        \n",
    "        \n",
    "        \n",
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room = False)\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-121.89, 37.29, 38.0, ..., 2, 4.625368731563422,\n",
       "        2.094395280235988],\n",
       "       [-121.93, 37.05, 14.0, ..., 5, 6.008849557522124,\n",
       "        2.7079646017699117],\n",
       "       [-117.2, 32.77, 31.0, ..., 2, 4.225108225108225,\n",
       "        2.0259740259740258],\n",
       "       ...,\n",
       "       [-116.4, 34.09, 9.0, ..., 3, 6.34640522875817, 2.742483660130719],\n",
       "       [-118.01, 33.82, 31.0, ..., 3, 5.50561797752809,\n",
       "        3.808988764044944],\n",
       "       [-122.45, 37.77, 52.0, ..., 3, 4.843505477308295,\n",
       "        1.9859154929577465]], dtype=object)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38</td>\n",
       "      <td>1568</td>\n",
       "      <td>351</td>\n",
       "      <td>710</td>\n",
       "      <td>339</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.62537</td>\n",
       "      <td>2.0944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14</td>\n",
       "      <td>679</td>\n",
       "      <td>108</td>\n",
       "      <td>306</td>\n",
       "      <td>113</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>6.00885</td>\n",
       "      <td>2.70796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-117.2</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31</td>\n",
       "      <td>1952</td>\n",
       "      <td>471</td>\n",
       "      <td>936</td>\n",
       "      <td>462</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.22511</td>\n",
       "      <td>2.02597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25</td>\n",
       "      <td>1847</td>\n",
       "      <td>371</td>\n",
       "      <td>1460</td>\n",
       "      <td>353</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.23229</td>\n",
       "      <td>4.13598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17</td>\n",
       "      <td>6592</td>\n",
       "      <td>1525</td>\n",
       "      <td>4459</td>\n",
       "      <td>1463</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.50581</td>\n",
       "      <td>3.04785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0      1   2     3     4     5     6       7           8  9        10  \\\n",
       "0 -121.89  37.29  38  1568   351   710   339  2.7042   <1H OCEAN  2  4.62537   \n",
       "1 -121.93  37.05  14   679   108   306   113  6.4214   <1H OCEAN  5  6.00885   \n",
       "2  -117.2  32.77  31  1952   471   936   462  2.8621  NEAR OCEAN  2  4.22511   \n",
       "3 -119.61  36.31  25  1847   371  1460   353  1.8839      INLAND  2  5.23229   \n",
       "4 -118.59  34.23  17  6592  1525  4459  1463  3.0347   <1H OCEAN  3  4.50581   \n",
       "\n",
       "        11  \n",
       "0   2.0944  \n",
       "1  2.70796  \n",
       "2  2.02597  \n",
       "3  4.13598  \n",
       "4  3.04785  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr = pd.DataFrame(housing_extra_attribs)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38</td>\n",
       "      <td>1568</td>\n",
       "      <td>351</td>\n",
       "      <td>710</td>\n",
       "      <td>339</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.62537</td>\n",
       "      <td>2.0944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14</td>\n",
       "      <td>679</td>\n",
       "      <td>108</td>\n",
       "      <td>306</td>\n",
       "      <td>113</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>6.00885</td>\n",
       "      <td>2.70796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.2</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31</td>\n",
       "      <td>1952</td>\n",
       "      <td>471</td>\n",
       "      <td>936</td>\n",
       "      <td>462</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.22511</td>\n",
       "      <td>2.02597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25</td>\n",
       "      <td>1847</td>\n",
       "      <td>371</td>\n",
       "      <td>1460</td>\n",
       "      <td>353</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.23229</td>\n",
       "      <td>4.13598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17</td>\n",
       "      <td>6592</td>\n",
       "      <td>1525</td>\n",
       "      <td>4459</td>\n",
       "      <td>1463</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.50581</td>\n",
       "      <td>3.04785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "17606   -121.89    37.29                 38        1568            351   \n",
       "18632   -121.93    37.05                 14         679            108   \n",
       "14650    -117.2    32.77                 31        1952            471   \n",
       "3230    -119.61    36.31                 25        1847            371   \n",
       "3555    -118.59    34.23                 17        6592           1525   \n",
       "\n",
       "      population households median_income ocean_proximity income_cat  \\\n",
       "17606        710        339        2.7042       <1H OCEAN          2   \n",
       "18632        306        113        6.4214       <1H OCEAN          5   \n",
       "14650        936        462        2.8621      NEAR OCEAN          2   \n",
       "3230        1460        353        1.8839          INLAND          2   \n",
       "3555        4459       1463        3.0347       <1H OCEAN          3   \n",
       "\n",
       "      rooms_per_household population_per_household  \n",
       "17606             4.62537                   2.0944  \n",
       "18632             6.00885                  2.70796  \n",
       "14650             4.22511                  2.02597  \n",
       "3230              5.23229                  4.13598  \n",
       "3555              4.50581                  3.04785  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs = pd.DataFrame(\n",
    "    housing_extra_attribs,\n",
    "    columns=list(housing.columns)+[\"rooms_per_household\", \"population_per_household\"],\n",
    "    index=housing.index)\n",
    "housing_extra_attribs.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12347</th>\n",
       "      <td>-116.54</td>\n",
       "      <td>33.82</td>\n",
       "      <td>12</td>\n",
       "      <td>9482</td>\n",
       "      <td>2501</td>\n",
       "      <td>2725</td>\n",
       "      <td>1300</td>\n",
       "      <td>1.5595</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>7.29385</td>\n",
       "      <td>2.09615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6263</th>\n",
       "      <td>-117.96</td>\n",
       "      <td>34.04</td>\n",
       "      <td>34</td>\n",
       "      <td>1381</td>\n",
       "      <td>265</td>\n",
       "      <td>1020</td>\n",
       "      <td>268</td>\n",
       "      <td>4.025</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.15299</td>\n",
       "      <td>3.80597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12208</th>\n",
       "      <td>-117.1</td>\n",
       "      <td>33.56</td>\n",
       "      <td>6</td>\n",
       "      <td>1868</td>\n",
       "      <td>289</td>\n",
       "      <td>750</td>\n",
       "      <td>247</td>\n",
       "      <td>4.3833</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>7.56275</td>\n",
       "      <td>3.03644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6396</th>\n",
       "      <td>-118.03</td>\n",
       "      <td>34.14</td>\n",
       "      <td>44</td>\n",
       "      <td>1446</td>\n",
       "      <td>250</td>\n",
       "      <td>721</td>\n",
       "      <td>243</td>\n",
       "      <td>4.7308</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "      <td>5.95062</td>\n",
       "      <td>2.96708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12601</th>\n",
       "      <td>-121.48</td>\n",
       "      <td>38.53</td>\n",
       "      <td>37</td>\n",
       "      <td>1704</td>\n",
       "      <td>361</td>\n",
       "      <td>902</td>\n",
       "      <td>356</td>\n",
       "      <td>1.9837</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>4.78652</td>\n",
       "      <td>2.53371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13354</th>\n",
       "      <td>-117.61</td>\n",
       "      <td>34.02</td>\n",
       "      <td>15</td>\n",
       "      <td>1791</td>\n",
       "      <td>346</td>\n",
       "      <td>1219</td>\n",
       "      <td>328</td>\n",
       "      <td>3.8125</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "      <td>5.46037</td>\n",
       "      <td>3.71646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5749</th>\n",
       "      <td>-118.27</td>\n",
       "      <td>34.16</td>\n",
       "      <td>45</td>\n",
       "      <td>1865</td>\n",
       "      <td>360</td>\n",
       "      <td>973</td>\n",
       "      <td>349</td>\n",
       "      <td>3.6587</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.34384</td>\n",
       "      <td>2.78797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18799</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>40.97</td>\n",
       "      <td>26</td>\n",
       "      <td>1183</td>\n",
       "      <td>276</td>\n",
       "      <td>513</td>\n",
       "      <td>206</td>\n",
       "      <td>2.225</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.74272</td>\n",
       "      <td>2.49029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15022</th>\n",
       "      <td>-117</td>\n",
       "      <td>32.77</td>\n",
       "      <td>30</td>\n",
       "      <td>1802</td>\n",
       "      <td>401</td>\n",
       "      <td>776</td>\n",
       "      <td>386</td>\n",
       "      <td>2.8125</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.66839</td>\n",
       "      <td>2.01036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16834</th>\n",
       "      <td>-122.55</td>\n",
       "      <td>37.59</td>\n",
       "      <td>31</td>\n",
       "      <td>1331</td>\n",
       "      <td>245</td>\n",
       "      <td>598</td>\n",
       "      <td>225</td>\n",
       "      <td>4.1827</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.91556</td>\n",
       "      <td>2.65778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>-121.99</td>\n",
       "      <td>37.96</td>\n",
       "      <td>17</td>\n",
       "      <td>2756</td>\n",
       "      <td>423</td>\n",
       "      <td>1228</td>\n",
       "      <td>426</td>\n",
       "      <td>5.5872</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "      <td>6.46948</td>\n",
       "      <td>2.88263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14906</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>32.6</td>\n",
       "      <td>33</td>\n",
       "      <td>905</td>\n",
       "      <td>205</td>\n",
       "      <td>989</td>\n",
       "      <td>222</td>\n",
       "      <td>2.7014</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.07658</td>\n",
       "      <td>4.45495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10779</th>\n",
       "      <td>-117.91</td>\n",
       "      <td>33.65</td>\n",
       "      <td>17</td>\n",
       "      <td>1328</td>\n",
       "      <td>377</td>\n",
       "      <td>762</td>\n",
       "      <td>344</td>\n",
       "      <td>2.2222</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>3.86047</td>\n",
       "      <td>2.21512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7934</th>\n",
       "      <td>-118.08</td>\n",
       "      <td>33.82</td>\n",
       "      <td>26</td>\n",
       "      <td>4259</td>\n",
       "      <td>588</td>\n",
       "      <td>1644</td>\n",
       "      <td>581</td>\n",
       "      <td>6.2519</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>7.33046</td>\n",
       "      <td>2.8296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9745</th>\n",
       "      <td>-121.7</td>\n",
       "      <td>36.67</td>\n",
       "      <td>37</td>\n",
       "      <td>641</td>\n",
       "      <td>129</td>\n",
       "      <td>458</td>\n",
       "      <td>142</td>\n",
       "      <td>3.3456</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.51408</td>\n",
       "      <td>3.22535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18768</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>40.51</td>\n",
       "      <td>23</td>\n",
       "      <td>2216</td>\n",
       "      <td>378</td>\n",
       "      <td>1006</td>\n",
       "      <td>338</td>\n",
       "      <td>4.559</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "      <td>6.55621</td>\n",
       "      <td>2.97633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5564</th>\n",
       "      <td>-118.29</td>\n",
       "      <td>33.91</td>\n",
       "      <td>41</td>\n",
       "      <td>2475</td>\n",
       "      <td>532</td>\n",
       "      <td>1416</td>\n",
       "      <td>470</td>\n",
       "      <td>3.8372</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.26596</td>\n",
       "      <td>3.01277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7064</th>\n",
       "      <td>-118.03</td>\n",
       "      <td>33.94</td>\n",
       "      <td>30</td>\n",
       "      <td>2572</td>\n",
       "      <td>521</td>\n",
       "      <td>1564</td>\n",
       "      <td>501</td>\n",
       "      <td>3.4861</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.13373</td>\n",
       "      <td>3.12176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13637</th>\n",
       "      <td>-117.32</td>\n",
       "      <td>34.08</td>\n",
       "      <td>41</td>\n",
       "      <td>1359</td>\n",
       "      <td>264</td>\n",
       "      <td>786</td>\n",
       "      <td>244</td>\n",
       "      <td>2.5208</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.56967</td>\n",
       "      <td>3.22131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4827</th>\n",
       "      <td>-118.32</td>\n",
       "      <td>34.03</td>\n",
       "      <td>31</td>\n",
       "      <td>2206</td>\n",
       "      <td>501</td>\n",
       "      <td>1194</td>\n",
       "      <td>435</td>\n",
       "      <td>1.9531</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>5.07126</td>\n",
       "      <td>2.74483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "12347   -116.54    33.82                 12        9482           2501   \n",
       "6263    -117.96    34.04                 34        1381            265   \n",
       "12208    -117.1    33.56                  6        1868            289   \n",
       "6396    -118.03    34.14                 44        1446            250   \n",
       "12601   -121.48    38.53                 37        1704            361   \n",
       "13354   -117.61    34.02                 15        1791            346   \n",
       "5749    -118.27    34.16                 45        1865            360   \n",
       "18799   -121.89    40.97                 26        1183            276   \n",
       "15022      -117    32.77                 30        1802            401   \n",
       "16834   -122.55    37.59                 31        1331            245   \n",
       "1468    -121.99    37.96                 17        2756            423   \n",
       "14906   -117.06     32.6                 33         905            205   \n",
       "10779   -117.91    33.65                 17        1328            377   \n",
       "7934    -118.08    33.82                 26        4259            588   \n",
       "9745     -121.7    36.67                 37         641            129   \n",
       "18768   -122.24    40.51                 23        2216            378   \n",
       "5564    -118.29    33.91                 41        2475            532   \n",
       "7064    -118.03    33.94                 30        2572            521   \n",
       "13637   -117.32    34.08                 41        1359            264   \n",
       "4827    -118.32    34.03                 31        2206            501   \n",
       "\n",
       "      population households median_income ocean_proximity income_cat  \\\n",
       "12347       2725       1300        1.5595          INLAND          2   \n",
       "6263        1020        268         4.025       <1H OCEAN          3   \n",
       "12208        750        247        4.3833       <1H OCEAN          3   \n",
       "6396         721        243        4.7308          INLAND          4   \n",
       "12601        902        356        1.9837          INLAND          2   \n",
       "13354       1219        328        3.8125          INLAND          3   \n",
       "5749         973        349        3.6587       <1H OCEAN          3   \n",
       "18799        513        206         2.225          INLAND          2   \n",
       "15022        776        386        2.8125       <1H OCEAN          2   \n",
       "16834        598        225        4.1827      NEAR OCEAN          3   \n",
       "1468        1228        426        5.5872          INLAND          4   \n",
       "14906        989        222        2.7014      NEAR OCEAN          2   \n",
       "10779        762        344        2.2222       <1H OCEAN          2   \n",
       "7934        1644        581        6.2519       <1H OCEAN          5   \n",
       "9745         458        142        3.3456       <1H OCEAN          3   \n",
       "18768       1006        338         4.559          INLAND          4   \n",
       "5564        1416        470        3.8372       <1H OCEAN          3   \n",
       "7064        1564        501        3.4861       <1H OCEAN          3   \n",
       "13637        786        244        2.5208          INLAND          2   \n",
       "4827        1194        435        1.9531       <1H OCEAN          2   \n",
       "\n",
       "      rooms_per_household population_per_household  \n",
       "12347             7.29385                  2.09615  \n",
       "6263              5.15299                  3.80597  \n",
       "12208             7.56275                  3.03644  \n",
       "6396              5.95062                  2.96708  \n",
       "12601             4.78652                  2.53371  \n",
       "13354             5.46037                  3.71646  \n",
       "5749              5.34384                  2.78797  \n",
       "18799             5.74272                  2.49029  \n",
       "15022             4.66839                  2.01036  \n",
       "16834             5.91556                  2.65778  \n",
       "1468              6.46948                  2.88263  \n",
       "14906             4.07658                  4.45495  \n",
       "10779             3.86047                  2.21512  \n",
       "7934              7.33046                   2.8296  \n",
       "9745              4.51408                  3.22535  \n",
       "18768             6.55621                  2.97633  \n",
       "5564              5.26596                  3.01277  \n",
       "7064              5.13373                  3.12176  \n",
       "13637             5.56967                  3.22131  \n",
       "4827              5.07126                  2.74483  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs.sample(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征缩放"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 最重要也是最需要应用在数据上的转换器，就是特征缩放，输入数值属性有很大的比例差异，会导致机器学习算法性能表现不佳\n",
    "# 房间总数 范围6到39320，收入中位数范围0到15\n",
    "# 目标值通常不需要缩放\n",
    "# 同比缩放所有属性，2种方法 最小-最大缩放和标准化\n",
    "# 最小-最大缩放，又叫归一化，将值重新缩放到0到1之间，将值减去最小值并除以最大值和最小值差，如果你不希望是0到1，可以调整超参数feature_range\n",
    "# 标准化 减去平均值 ，所以标准化均值总是0，然后除以方差，结果的分布具备单位方差，不同于归一化，标准化不将值绑定到特定范围，受异常值影响小\n",
    "# 缩放器仅用来拟合训练集，不是完成的数据集\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 转换流水线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 许多数据转换的步骤需要以正确的顺序来执行， PipeLine来支持这样的转换\n",
    "# pipline构造函数会通过一系列名称/估算器的配对来定义步骤的序列，必须是转换器，必须有fit_fransform()方法\n",
    "# 调用流水芡的fit方法时，会在所有转换器上按照顺序依次调用fit_transform()，将一个调用的输出作为参数传递给下一个调用方法，直到传递到最终\n",
    "# 估算器，只会调用fit方法\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler())\n",
    "    ])\n",
    "\n",
    "housing_num_tr = num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 9 columns):\n",
      "longitude             16512 non-null float64\n",
      "latitude              16512 non-null float64\n",
      "housing_median_age    16512 non-null float64\n",
      "total_rooms           16512 non-null float64\n",
      "total_bedrooms        16354 non-null float64\n",
      "population            16512 non-null float64\n",
      "households            16512 non-null float64\n",
      "median_income         16512 non-null float64\n",
      "income_cat            16512 non-null category\n",
      "dtypes: category(1), float64(8)\n",
      "memory usage: 1.1 MB\n"
     ]
    }
   ],
   "source": [
    "housing_num.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataFrame -> series -> ndarray\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['longitude',\n",
       " 'latitude',\n",
       " 'housing_median_age',\n",
       " 'total_rooms',\n",
       " 'total_bedrooms',\n",
       " 'population',\n",
       " 'households',\n",
       " 'median_income',\n",
       " 'income_cat']"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_attribs =list(housing_num) \n",
    "num_attribs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "num_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(num_attribs)),\n",
    "        ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin \n",
    "class MyLabelBinarizer(TransformerMixin):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        self.encoder = LabelBinarizer(*args, **kwargs)\n",
    "    def fit(self, x, y=0):\n",
    "        self.encoder.fit(x)\n",
    "        return self\n",
    "    def transform(self, x, y=0):\n",
    "        return self.encoder.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_attribs = ['ocean_proximity']\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(cat_attribs)),               \n",
    "        ('LabelBinarizer', MyLabelBinarizer()),\n",
    "    ])\n",
    "\n",
    "# housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "\n",
    "full_pipeline = FeatureUnion(transformer_list=[\n",
    "        (\"num_pipline\", num_pipeline,),\n",
    "        ('cat_pipline', cat_pipeline),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 1.18684903, -1.34218285,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       ...,\n",
       "       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.43579109,  0.99645926,  1.85670895, ...,  0.        ,\n",
       "         1.        ,  0.        ]])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared = full_pipeline.fit_transform(housing)\n",
    "housing_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.312055</td>\n",
       "      <td>-0.086499</td>\n",
       "      <td>0.155318</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.176025</td>\n",
       "      <td>0.659695</td>\n",
       "      <td>-1.165317</td>\n",
       "      <td>-0.908967</td>\n",
       "      <td>-1.036928</td>\n",
       "      <td>-0.998331</td>\n",
       "      <td>-1.022227</td>\n",
       "      <td>1.336459</td>\n",
       "      <td>1.890305</td>\n",
       "      <td>0.217683</td>\n",
       "      <td>-0.033534</td>\n",
       "      <td>-0.836289</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.186849</td>\n",
       "      <td>-1.342183</td>\n",
       "      <td>0.186642</td>\n",
       "      <td>-0.313660</td>\n",
       "      <td>-0.153345</td>\n",
       "      <td>-0.433639</td>\n",
       "      <td>-0.093318</td>\n",
       "      <td>-0.532046</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.465315</td>\n",
       "      <td>-0.092405</td>\n",
       "      <td>0.422200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.017068</td>\n",
       "      <td>0.313576</td>\n",
       "      <td>-0.290520</td>\n",
       "      <td>-0.362762</td>\n",
       "      <td>-0.396756</td>\n",
       "      <td>0.036041</td>\n",
       "      <td>-0.383436</td>\n",
       "      <td>-1.045566</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.079661</td>\n",
       "      <td>0.089736</td>\n",
       "      <td>-0.196453</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.492474</td>\n",
       "      <td>-0.659299</td>\n",
       "      <td>-0.926736</td>\n",
       "      <td>1.856193</td>\n",
       "      <td>2.412211</td>\n",
       "      <td>2.724154</td>\n",
       "      <td>2.570975</td>\n",
       "      <td>-0.441437</td>\n",
       "      <td>-0.006202</td>\n",
       "      <td>-0.357834</td>\n",
       "      <td>-0.004194</td>\n",
       "      <td>0.269928</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\n",
       "0 -1.156043  0.771950  0.743331 -0.493234 -0.445438 -0.636211 -0.420698   \n",
       "1 -1.176025  0.659695 -1.165317 -0.908967 -1.036928 -0.998331 -1.022227   \n",
       "2  1.186849 -1.342183  0.186642 -0.313660 -0.153345 -0.433639 -0.093318   \n",
       "3 -0.017068  0.313576 -0.290520 -0.362762 -0.396756  0.036041 -0.383436   \n",
       "4  0.492474 -0.659299 -0.926736  1.856193  2.412211  2.724154  2.570975   \n",
       "\n",
       "         7         8         9         10        11   12   13   14   15   16  \n",
       "0 -0.614937 -0.954456 -0.312055 -0.086499  0.155318  1.0  0.0  0.0  0.0  0.0  \n",
       "1  1.336459  1.890305  0.217683 -0.033534 -0.836289  1.0  0.0  0.0  0.0  0.0  \n",
       "2 -0.532046 -0.954456 -0.465315 -0.092405  0.422200  0.0  0.0  0.0  0.0  1.0  \n",
       "3 -1.045566 -0.954456 -0.079661  0.089736 -0.196453  0.0  1.0  0.0  0.0  0.0  \n",
       "4 -0.441437 -0.006202 -0.357834 -0.004194  0.269928  1.0  0.0  0.0  0.0  0.0  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared = pd.DataFrame(housing_prepared)\n",
    "housing_prepared.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选择和训练模型\n",
    "* 获得了数据\n",
    "* 数据探索\n",
    "* 对训练集和测试集进行拆分\n",
    "* 编写了转换数据流水线\n",
    "* 自动清理和准备机器学习算法的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17606    286600.0\n",
       "18632    340600.0\n",
       "14650    196900.0\n",
       "3230      46300.0\n",
       "3555     254500.0\n",
       "19480    127900.0\n",
       "8879     500001.0\n",
       "13685    140200.0\n",
       "4937      95000.0\n",
       "4861     500001.0\n",
       "16365     92100.0\n",
       "19684     61500.0\n",
       "19234    313000.0\n",
       "13956     89000.0\n",
       "2390     123900.0\n",
       "11176    197400.0\n",
       "15614    500001.0\n",
       "2953      63300.0\n",
       "13209    107000.0\n",
       "6569     184200.0\n",
       "5825     280900.0\n",
       "18086    500001.0\n",
       "16718    171300.0\n",
       "13600    116600.0\n",
       "13989     60800.0\n",
       "15168    121100.0\n",
       "6747     270700.0\n",
       "7398     109900.0\n",
       "5562     159600.0\n",
       "16121    500001.0\n",
       "           ...   \n",
       "12380    122500.0\n",
       "5618     350000.0\n",
       "10060    172800.0\n",
       "18067    500001.0\n",
       "4471     146600.0\n",
       "19786     81300.0\n",
       "9969     247600.0\n",
       "14621    164100.0\n",
       "579      254900.0\n",
       "11682    185700.0\n",
       "245      126800.0\n",
       "12130    114200.0\n",
       "16441    101800.0\n",
       "11016    265600.0\n",
       "19934     88900.0\n",
       "1364     225000.0\n",
       "1236     123500.0\n",
       "5364     500001.0\n",
       "11703    321600.0\n",
       "10356    266000.0\n",
       "15270    346700.0\n",
       "3754     190200.0\n",
       "12166    148800.0\n",
       "6003     214800.0\n",
       "7364     174300.0\n",
       "6563     240200.0\n",
       "12053    113000.0\n",
       "13908     97800.0\n",
       "11159    225900.0\n",
       "15775    500001.0\n",
       "Name: median_house_value, Length: 16512, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(housing_prepared)\n",
    "df.head()\n",
    "housing_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  训练模型和评估训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [203682.37379543 326371.39370781 204218.64588245  58685.4770482\n",
      " 194213.06443039]\n"
     ]
    }
   ],
   "source": [
    "# 预测数据\n",
    "some_data = housing.iloc[:5]\n",
    "some_labels = housing_labels.iloc[:5]\n",
    "\n",
    "some_data_prepared = full_pipeline.transform(some_data)\n",
    "\n",
    "print(\"Predictions:\", lin_reg.predict(some_data_prepared))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68376.64295459937"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "housing_predictions = lin_reg.predict(housing_prepared)\n",
    "lin_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "lin_mse\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse\n",
    "\n",
    "# 大多数地区的房屋中位数 在120000到265000美元之间，预测误差高达 68628,这是一个典型的模型对训练数据拟合不足的案例，\n",
    "# 原因可能是特征无法提供足够的信息来做出更好的预测，或者模型本身不够强大，\n",
    "# 1. 选择强大的模型，2 为算法提供更好的特征，3.减少对模型的限制等方法，\n",
    "\n",
    "# 决策树可以找到复杂的非线性关系\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=42, splitter='best')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(random_state=42)\n",
    "tree_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = tree_reg.predict(housing_prepared)\n",
    "tree_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    " # 完美，也可能是这个模型对数据严重过度拟合了，如何确认？轻易不要启动测试集，拿训练集中的一部分用于训练，另一部分用于模型的验证"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用交叉验证来更好的进行评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([70307.54079165, 67270.94561829, 71361.89886493, 68889.91784739,\n",
       "       70992.94150631, 74140.66907294, 70607.72083897, 70576.13836762,\n",
       "       76433.16342183, 70270.96266671])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用train_test_split函数将训练集分为较小的训练集和验证集，然后根据这些较小的训练集来训练模型，并对其进行评估\n",
    "# sklearn的交叉验证，将训练集随机分割成10个不同的子集，每个子集称为一个折叠，对模型进行10次训练和评估，每次挑选1个折叠进行评估，另外9个进行训练\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# neg_mean_squared_error‘ 也就是 均方差回归损失 该统计参数是预测数据和原始数据对应点误差的平方和的均值\n",
    "\n",
    "# 决策树交叉验证\n",
    "scores = cross_val_score(tree_reg, housing_prepared, housing_labels,\n",
    "                         scoring=\"neg_mean_squared_error\", cv=10)\n",
    "tree_rmse_scores = np.sqrt(-scores)\n",
    "tree_rmse_scores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [70307.54079165 67270.94561829 71361.89886493 68889.91784739\n",
      " 70992.94150631 74140.66907294 70607.72083897 70576.13836762\n",
      " 76433.16342183 70270.96266671]\n",
      "Mean: 71085.189899664\n",
      "Standard deviation: 2431.961029690467\n"
     ]
    }
   ],
   "source": [
    "def display_scores(scores):\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean:\", scores.mean())\n",
    "    print(\"Standard deviation:\", scores.std())\n",
    "\n",
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [66877.52325028 66608.120256   70575.91118868 74179.94799352\n",
      " 67683.32205678 71103.16843468 64782.65896552 67711.29940352\n",
      " 71080.40484136 67687.6384546 ]\n",
      "Mean: 68828.99948449328\n",
      "Standard deviation: 2662.7615706103443\n"
     ]
    }
   ],
   "source": [
    "# 线性 交叉验证\n",
    "lin_scores = cross_val_score(lin_reg, housing_prepared, housing_labels,\n",
    "                             scoring=\"neg_mean_squared_error\", cv=10)\n",
    "lin_rmse_scores = np.sqrt(-lin_scores)\n",
    "display_scores(lin_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "           oob_score=False, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators=10, random_state=42)\n",
    "forest_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22112.540875989125"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = forest_reg.predict(housing_prepared)\n",
    "forest_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "forest_rmse = np.sqrt(forest_mse)\n",
    "forest_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [51513.5500248  48821.99364978 53451.63889205 54910.55855153\n",
      " 50405.68166817 56772.90691171 51909.21824137 50516.14893695\n",
      " 55699.80788403 53107.7805515 ]\n",
      "Mean: 52710.92853118938\n",
      "Standard deviation: 2414.7271791176026\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "forest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,\n",
    "                                scoring=\"neg_mean_squared_error\", cv=10)\n",
    "forest_rmse_scores = np.sqrt(-forest_scores)\n",
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型调参和网格搜索\n",
    "   1. 手动调整超参数，找到很好的组合很困难\n",
    "   2. 使用GridSearchCV替你进行搜索，告诉它，进行试验的超参数是什么，和需要尝试的值，它会使用交叉验证评估所有超参数的可能组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "           oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid=[{'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]}, {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
    "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
    "  ]\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "                           scoring='neg_mean_squared_error', return_train_score=True)\n",
    "grid_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 6, 'n_estimators': 30}"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features=6, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=30, n_jobs=1, oob_score=False, random_state=42,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64265.85210091941 {'max_features': 2, 'n_estimators': 3}\n",
      "55882.984840186626 {'max_features': 2, 'n_estimators': 10}\n",
      "53472.52977048399 {'max_features': 2, 'n_estimators': 30}\n",
      "61320.61721024631 {'max_features': 4, 'n_estimators': 3}\n",
      "53834.66617027598 {'max_features': 4, 'n_estimators': 10}\n",
      "51273.25987325864 {'max_features': 4, 'n_estimators': 30}\n",
      "59851.160077252265 {'max_features': 6, 'n_estimators': 3}\n",
      "53108.49267924178 {'max_features': 6, 'n_estimators': 10}\n",
      "50804.490677496164 {'max_features': 6, 'n_estimators': 30}\n",
      "59225.21977850634 {'max_features': 8, 'n_estimators': 3}\n",
      "52883.78258097852 {'max_features': 8, 'n_estimators': 10}\n",
      "50942.15913785863 {'max_features': 8, 'n_estimators': 30}\n",
      "62801.35724701795 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54452.705621479254 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "61122.949491813 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "53014.33296453683 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "60252.65376680052 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "52716.55059637336 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "cvres = grid_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据准备步骤也可以当作超参数来处理，网格搜索会自动查找是否添加你不确定的特征，比如是否使用转换器 combinedAttre的超参数add_bedrooms_per_rom\n",
    "# 也可是使用它自动寻找处理问题的最佳方法，比如异常值，缺失特征，特征选择等"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机搜索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise',\n",
       "          estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "           oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid=True, n_iter=10, n_jobs=1,\n",
       "          param_distributions={'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1a19734b38>, 'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1a19734128>},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring='neg_mean_squared_error',\n",
       "          verbose=0)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_distribs = {\n",
    "        'n_estimators': randint(low=1, high=200),\n",
    "        'max_features': randint(low=1, high=8),\n",
    "    }\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,\n",
    "                                n_iter=10, cv=5, scoring='neg_mean_squared_error', random_state=42)\n",
    "rnd_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49934.10665014475 {'max_features': 7, 'n_estimators': 180}\n",
      "52141.22470034282 {'max_features': 5, 'n_estimators': 15}\n",
      "51460.36758765149 {'max_features': 3, 'n_estimators': 72}\n",
      "51393.456447770266 {'max_features': 5, 'n_estimators': 21}\n",
      "50042.086256281895 {'max_features': 7, 'n_estimators': 122}\n",
      "51433.89711030478 {'max_features': 3, 'n_estimators': 75}\n",
      "51285.0826463537 {'max_features': 3, 'n_estimators': 88}\n",
      "50317.30205667026 {'max_features': 5, 'n_estimators': 100}\n",
      "50995.16394036666 {'max_features': 3, 'n_estimators': 150}\n",
      "65494.67709619073 {'max_features': 5, 'n_estimators': 2}\n"
     ]
    }
   ],
   "source": [
    "cvres = rnd_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析最佳模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.79326113e-02, 6.18280724e-02, 4.33395023e-02, 1.81017027e-02,\n",
       "       1.83291556e-02, 1.93269892e-02, 1.78369580e-02, 2.41360490e-01,\n",
       "       1.61976585e-01, 5.35982558e-02, 1.06273526e-01, 6.14045141e-02,\n",
       "       1.22353255e-02, 1.08821239e-01, 2.76143239e-05, 2.59938294e-03,\n",
       "       5.00807682e-03])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.24136048955382886, 'median_income'),\n",
       " (0.16197658459849276, 'income_cat'),\n",
       " (0.10882123891274476, 'INLAND'),\n",
       " (0.10627352591969835, 'population_per_household'),\n",
       " (0.06793261134305183, 'longitude'),\n",
       " (0.06182807241916786, 'latitude'),\n",
       " (0.061404514078416045, 'bedrooms_per_room'),\n",
       " (0.05359825584988402, 'rooms_per_household'),\n",
       " (0.04333950231438806, 'housing_median_age'),\n",
       " (0.019326989179411204, 'population'),\n",
       " (0.018329155582427956, 'total_bedrooms'),\n",
       " (0.01810170268968371, 'total_rooms'),\n",
       " (0.01783695799011688, 'households'),\n",
       " (0.012235325483341324, '<1H OCEAN'),\n",
       " (0.0050080768169210735, 'NEAR OCEAN'),\n",
       " (0.002599382944523225, 'NEAR BAY'),\n",
       " (2.7614323902184926e-05, 'ISLAND')]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# num_attribs\n",
    "extra_attribs = [\"rooms_per_household\", \"population_per_household\", \"bedrooms_per_room\"]\n",
    "cat_one_hot_attribs = list(encoder.classes_)\n",
    "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
    "sorted(zip(feature_importances, attributes), reverse = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 通过测试集评估系统\n",
    "   1. 从测试集中获取预测器和标签\n",
    "   2. 运行full_pipline来转换数据\n",
    "   3. 在测试集上评估最终模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49036.58642724182"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = grid_search.best_estimator_\n",
    "\n",
    "X_test = strat_test_set.drop('median_house_value', axis = 1)\n",
    "y_test = strat_test_set['median_house_value'].copy()\n",
    "\n",
    "X_test_prepared = full_pipeline.transform(X_test)\n",
    "# X_test_prepared\n",
    "\n",
    "# df = pd.DataFrame(X_test_prepared)\n",
    "# df.head()\n",
    "\n",
    "final_predictions = final_model.predict(X_test_prepared)\n",
    "\n",
    "final_mse = mean_squared_error(y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "final_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 项目启动阶段\n",
    "   1. 展示解决方案 学习了什么\n",
    "   2. 什么有用\n",
    "   3. 什么没有用\n",
    "   4. 基于什么假设\n",
    "   5. 以及系统的限制有哪些\n",
    "   6. 制作漂亮的演示文稿，例如收入中位数是预测房价的首要指标"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 启动，监控和维护系统\n",
    "   1. 为生产环境做好准备，将生产数据源接入系统\n",
    "   2. 编写监控代码，定期检查系统的实时性能，性能下降时触发警报，系统崩溃和性能退化\n",
    "   3. 时间推移，模型会渐渐腐坏，定期使用新数据训练模型\n",
    "\n",
    "## 评估系统性能 \n",
    "   1. 需要对系统的预测结果进行抽样评估，需要人工分析，分析师可能是专家，平台工作人员，都需要将人工评估的流水线接入你的系统\n",
    "   2. 评估输入系统的数据质量\n",
    "   3. 使用新鲜数据定期训练你的模型，最多6个月"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 总结\n",
    "    本周主要学习 数据准备，构建监控工作，建立人工评估流水线，自动化定期训练模型上，熟悉整个机器学习流程，\n",
    "## 建议\n",
    "    选择一个数据集，尝试从A到Z的整个过程，从竞赛网站上，选择一个数据集，一个明确目标，以及可以一起分享经验的同伴\n",
    "    https://www.kaggle.com/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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